This demo introduces WaveBox, a dynamic, software-defined waveform generation system developed to assess the resilience of communication networks against many types of interference scenarios. WaveBox features seamless waveform switching, allowing users to efficiently adjust interference patterns to adapt to diverse operational scenarios. We will showcase the system's effectiveness and versatility, highlighting its ability to adapt to evolving mission requirements. Additionally, the system's intuitive graphical user interface (GUI) supports rapid waveform adjustments, enhancing its responsiveness in dynamic environments. WaveBox can provide a flexible software-defined tool for evaluating the robustness of wireless systems.

We design and implement a new multiple-input multiple-output (MIMO) frequency-division-duplex (FDD) spread-spectrum link and demonstrate real-time high-definition (HD) video streaming over a Radio Frequency System-on-a-Chip (RFSoC) software-radio testbed. To the best of our knowledge, this is the first-of-its-kind high-throughput low-latency full-duplex spread-spectrum link on RFSoC platforms which demonstrates an aggregated data throughput of 122 Mbps that supports real-time recording and playback of uncompressed full-HD video. The testbed comprises two Xilinx Zynq Ultrascale+ RFSoC ZCU111 evaluation kits with a custom-built application layer. A host-based graphical user interface (GUI) demonstrates live performance of the proposed 4×4 MIMO wireless link in terms of error vector magnitudepre-detection SINR and bit error rate (BER) and enables on-the-fly reconfiguration of link parameters such as spreading code sequence, transmit/receive antenna gains.

In highly dynamic and contested RF environments, communication systems must swiftly adapt to fluctuating spectral conditions while ensuring network quality of service (QoS). Maintaining link synchronization and spectral efficiency during waveform adaptation is particularly challenging due to the high mobility and autonomy of devices, coupled with the possibility of operating in GPS-denied environments. In this demo, we introduce a scalable, high-speed FPGA-based parallel decoding algorithm that leverages the HORNets signature adaptation protocol to address these challenges. Our solution preserves link synchronization within a multi-node network and enables efficient, lightweight waveform adaptation without reliance on GPS. The algorithm's resilience and effectiveness are demonstrated using a three-node cluster configuration, all subjected to non-colored or colored intentional interference.

We consider the problem of interference avoidance in orthogonal frequency-division multiplexing (OFDM) communication systems. Unlike conventional OFDM systems that rely on static waveform designs, we propose to apply a coding sequence at the input of the inverse discrete Fourier transform (IDFT) operator to digitally shape the transmitted OFDM waveform. By dynamically optimizing the coding sequence to maximize the signal-to-interference-plus-noise ratio (SINR) at the receiver, the proposed approach enables resilient communication in heavily congested spectral environments in a technically simple and efficient manner. We carry out extensive simulation studies to evaluate the performance of the proposed system under various interference scenarios and demonstrate significant improvements in SINR when compared to conventional OFDM systems. When the proposed dynamic OFDM waveform optimization process is fielded in commercially available software-defined radio platforms, the transceiver can evade rapidly changing (msec–scale or faster) interference and survive in contested spectral environments.

Support vector machines (SVMs) have been the learning model of choice in numerous classification applications. While SVMs are widely successful in real-world deployments, they remain susceptible to mislabeled examples in training datasets where the presence of few faults can severely affect decision boundaries, thereby affecting the model's performance on unseen data. In this brief, we develop and describe in implementation detail a novel method based on $L_{1}$ -norm principal-component data analysis and geometry that aims to filter out atypical data instances on a class-by-class basis before the training phase of SVMs and thus provide the classifier with robust support-vector candidates for making classification boundaries. The proposed dataset curation method is entirely data-driven (touch-free), unsupervised, and computationally efficient. Extensive experimental studies on real datasets included in this brief illustrate the $L_{1}$ -norm curation method and demonstrate its efficacy in protecting SVM models from data faults during learning.

We consider the problem of dynamically optimizing a multiple-input multiple-output (MIMO) wireless waveform in a given potentially heavily utilized fixed frequency band with applications in near-field or far-field autonomous machine-to-machine communications. In particular, we find the transmitter beam weight vector and the pulse code sequence that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR joint space-time receiver filter. We propose and derive two novel model-based solutions: (a) Disjoint, space first (transmit weight vector) then time (pulse code sequence) waveform optimization and (b) jointly optimal transmit weight vector and pulse code sequence optimization (a mixed integer programming problem.) The proposed formally derived algorithmic solutions are studied in extensive simulations under varying waveform code length, near-field/far-field and spread-spectrum/ non-spread-spectrum interference, in light and dense interference scenarios. Our findings highlight the effectiveness of the described methods compared to static conventionally designed MIMO links and the remarkable ability of the joint space-time optimized waveforms to avoid heavy interference.

Fast, robust, high-accuracy localization is a key enabler for future location-aware applications in streetscape communication networks and next-generation networked autonomous agents. Specifically, massive multiple-input and multiple-output (MIMO) antenna systems have received increasing attention due to high angular resolution. However, in dense multipath environments, such as urban areas, pure direction-of-arrival (DoA)-based techniques have not been very popular due to large localization errors.In this paper, we present and evaluate, on real measurements from the POWDER-RENEW platform, a novel method to carry out DoA estimation from just one antenna array snapshot. The measurements are taken from an indoor testbed that is based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system. Experimental results – in the presence of spatial aliasing – show that for certain emitter locations our proposed universal one-shot DoA estimator outperforms in azimuth/elevation accuracy state-of-the-art subspace-based methods that involve collection of a sufficiently large data record of antenna array snapshots.

Wireless remote control of a single or a group of underwater vehicles by a single human operator offers the opportunity to collect more real-time data than a single ship or vehicle. Commercial underwater modems are often too large to fit small-size submersibles, prohibitively expensive for large-scale deployments, and typically closed source which limits their compatibility with other sensors and therefore their application in research. In this work, we focus on establishing wireless communication between a remotely operated vehicle (ROV) and a surface station using an in-house built low-size, weight, power, and cost underwater acoustic modem and an affordable underwater ROV. To assess wireless communication performance we built a high-fidelity simulation framework in an underwater robotics simulator. Additionally, we verify the simulation with physical tests of the ROV and modems. Finally, to evaluate the feasibility of deploying a fleet of ROVs we designed and built a small, lightweight, low-cost ROV. This ROV will serve as the platform to test connected underwater robotics technology in future work.

In this article we explore new techniques that can enable open remote experimentation for mobile networks. We first propose a cloud-based framework called CloudRAFT, based on which experimenters are allowed to remotely access and control experimental resources via public cloud AWS and share the resulting data and code via the cloud. Then, we discuss the enabling techniques for CloudRAFT, including Amazon serverless service, VNC-based remote command line, and Websocket-based real time communications, among others. Finally, we showcase the application of these techniques in enabling remote access to UB NeXT, a software-defined testbed that has been developed at University at Buffalo for wireless mobile network modeling, optimization and deployment. This work verifies the feasibility of accessing, controlling and sharing wireless testbeds through a remote public cloud.

We introduce RF-SITL, a radio frequency (RF) software-in-the-loop (SITL) channel emulator developed with GNU Radio and the University at Buffalo’s Airborne Networking and Communications (UB-ANC) emulator to enable integrated simulation of systems comprising multiple unmanned aerial vehicles (UAVs) interacting over a wireless communication channel. RF-SITL could be paired with any multi-robot simulator to enable I/Q sample-level fidelity simulation of communication interactions between the robots by accurately simulating channel effects, including interference, noise, distance-dependent path loss, and packet losses. RF-SITL works as follows: 1) it instantiates a virtual software-defined transceiver in GNU Radio for each UAV simulated in the UB-ANC Emulator; 2) it builds an interference channel model in which each network node receives the superposition of signals transmitted from other nodes; and 3) it synchronizes the location of each simulated UAV in the UB-ANC Emulator with the virtualized RF transceivers in RF-SITL, such that the communication channel between nodes can accurately model distance-dependent channel effects, such as path loss. With these capabilities, we can use both off-the-shelf and custom-built signal processing flowgraphs that simulate Gaussian Minimum Shift Keying (GMSK), 802.11-like Orthogonal Frequency Division Multiplexing (OFDM), and direct sequence spread-spectrum (DSSS) links in GNU Radio to simulate swarm UAV networks prior to their deployment in software-defined radios in a swarm UAV network.

As methods for detecting hidden data evolve, there exits an ever increasing need to develop new steganographic solutions. This paper introduces novel spread spectrum (SS) and improved spread spectrum (ISS) multimedia data embedding techniques using L1principal component signatures. The design presented performs well in terms of bit error rate and the structural similarity index metric.

We prove, for the first time in the literature of communication theory and machine learning, the equivalence of joint maximum-likelihood (ML) optimal channel estimation and data detection (JOCEDD) to the problem of finding the L1-norm principal components of a real-valued data matrix. Optimal algorithms for L1-norm principal component analysis (PCA) are therefore direct solvers to the problem of interest, thus the proposed JOCEDD approach requires a polynomial number of operations. To avoid high computational costs incurred by the exact calculation of optimal L1principal components, we implement an efficient bit flipping-based algorithm for L1-norm PCA in a software-defined radio. In particular, we carry out experiments with two radios that operate at Wi-Fi frequencies in a multipath indoor radio environment and have no direct line-of-sight. We apply L1-norm PCA for JOCEDD over short frames that are transmitted over the single-input single-output communication link. We compare the performance of supervised data-aided channel estimation techniques versus JOCEDD in terms of bit-error-rate and demonstrate the superiority of the proposed approach across a wide range of signal-to-noise ratios.

Terahertz (THz)-band (0.1 THz to 10 THz) communication is envisioned as a key technology to meet the demand for faster, more ubiquitous wireless communication networks. For many years, the lack of compact, fast and efficient ways to generate, modulate, detect and demodulate THz-band signals has limited the feasibility of such communication systems. Recently, major progress within different device technologies is finally closing the so-called THz gap. For the time being, communication testbeds have been developed at sub-THz frequencies, i.e., at or near the boundary with millimeter-wave communication systems. Nonetheless, higher carrier frequencies and their associated bandwidth are needed to meet the demand for much higher data rates. In this paper, the TeraNova platform, i.e., the first integrated testbed for ultra-broadband wireless communications at true THz-band frequencies, is presented. The system consists of a transmitter and a receiver based on Schottky-diode frequency multiplying and mixing chains able to up & down-convert an information-bearing intermediate frequency (IF) signal up to 40 GHz-wide between 1 and 1.05 THz, i.e., the first absorption-defined transmission window above 1 THz. Guided by the experimental characterization of the THz channel in terms of path-loss and noise, tailored framing, time synchronization, channel estimation and single- and multi-carrier modulation techniques are implemented in software and realized by a state-of-the-art arbitrary waveform generator and a digital storage oscilloscope at the transmitter and the receiver, respectively. Experimental results are presented herein to highlight the opportunities and challenges to unleash the potential of the THz band.

We envision dense network deployments of Internet-of-Things (IoT) connected devices that report data to a common base station (BS). The devices utilize repeats of a basic shaping pulse occupying the entire continuum of the device-accessible spectrum. We propose an optimal algorithm to adaptively design sparse waveforms with well-placed energy that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear filter at the BS. Additionally, we propose a computationally efficient suboptimal waveform design algorithm for the same problem. Simulation studies show that the proposed waveform designs attain superior pre-detection SINR performance than conventional binary, quaternary, and sparse-binary/quaternary waveform designs, thus offering a promising PHY-MAC approach to maintain wireless connectivity in overloaded network setups.

We consider the problem of interference avoidance via all-spectrum digital waveform design in wireless communication links that operate in multipath fading environments. Specifically, we select a square-root raised cosine pulse-shaping signal that occupies all-hardware accessible frequency bandwidth. We propose an algorithm that optimizes a sequence of L sign/phase-shifted repeats of the basic shaping pulse to form the all-spectrum digital waveform that will carry our information symbols. The sequence can take values from either binary or quaternary alphabets. We propose to optimize the specific values of the sign/phase shift sequence via bit flipping such that the signal-to-interference-plus-noise ratio (SINR) at the output of the max-SINR linear receiver is maximized at any given time. The complexity of the proposed algorithm is O( L3) and is independent of the alphabet size. Simulation studies demonstrate that the proposed digital waveform designs achieve practically the same SINR post-filtering performance with max-SINR optimal waveforms designed via exhaustive search.

We consider the problem of blind channel estimation with minimal pilot signaling in multi-cell multi-user MIMO systems with very large antenna arrays at the base station. We develop a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates in short-data record multicell massive MIMO environments with no prior channel state information. The proposed algorithm utilizes a novel initialization step that is based on auxiliary-vector (AV) subspace decomposition. Simulation studies show that for pilot signaling of about 4%, information data extraction can be achieved with lower probability of error than eigendecomposition-based initialization techniques, while for observation records of sufficient length it nearly attains the error rate performance achieved with complete knowledge of the channels.

Compressed sensing (CS) is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Exact reconstruction may then be possible with much fewer than the Nyquist-required number of data. In this paper, we consider a multiview video system in which multiple cameras at different locations perform independent CS to simultaneously capture different views of a scene. At the decoder, we propose a disparity- and motion-compensated total variation minimization algorithm to jointly reconstruct the multiview video sequence. The experimental results show that the proposed joint reconstruction algorithm successfully exploits simultaneously intra-frame, inter-frame, and inter-view sparsity and significantly outperforms existing independent-view reconstruction, residue-view reconstruction, and motion-adaptive reconstruction algorithms.

We present an algorithm that computes exactly (optimally) the S-sparse (1≤S1-norm-projection principal component of a real-valued data matrix X ∈ ℝD×Nthat contains N samples of dimension D. For fixed sample support N, the optimal L1-sparse algorithm has linear complexity in data dimension, O(D). For fixed dimension D (thus, fixed sparsity S), the optimal L1-sparse algorithm has polynomial complexity in sample support, O(NS). Numerical studies included in this paper illustrate the theoretical developments and demonstrate the remarkable robustness to faulty data/measurements of the calculated sparse-L1principal components.

We introduce maximum-SINR sparse-binary waveforms that modulate data information symbols from any finite alphabet and span the whole continuum of the available/device-accessible spectrum. We offer an optimal algorithm that designs the proposed waveforms by maximizing the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR linear receiver. In addition, we offer a suboptimal algorithm for the same problem with significantly reduced computational complexity. The post-filtering SINR improvements attained by the proposed waveforms in a single-input single-output (SISO) communication system with colored interference are presented analytically. Simulation studies compare the proposed waveforms with their conventional non-sparse counterparts and demonstrate their superior SINR performance.

We introduce X60, the first SDR-based testbed for 60 GHz WLANs, featuring fully programmable MAC/PHY/Network layers, multi-Gbps rates, and a user-configurable 12-element phased antenna array. These features provide us with an unprecedented opportunity to revisit the most important aspects of 60 GHz signal propagation and obtain new insights on performance expected from practical 60 GHz systems. X60's unique capabilities make it an ideal platform for experimentation and prototyping across layers.

We develop a novel joint-view Kalman filter for causal reconstruction of compressed-sensed multiview videos. Compressed-sensed multiview video frames are initially reconstructed individually via ℓ1-norm minimization. Then, ajoint-view state transition model is established for each pair of neighboring views using motion or motion-disparity field estimates. Experimental results demonstrate significantly improved reconstruction quality compared to conventional CS reconstruction and independent-view (single-view) motion-compensated Kalman filtering.

The radio frequency (RF) spectrum becomes overly crowded in some indoor environments due to the high density of users and bandwidth demands. To accommodate the tremendous wireless data demands, efficient spectrum-sharing approaches are highly desired. To this end, this paper introduces a new spectrum sharing solution for indoor environments based on the usage of a reconfigurable reflect-array in the middle of the wireless channel. By optimally controlling the phase shift of each element on the reflect-array, the useful signals for each transmission pair can be enhanced while the interferences can be canceled. As a result, multiple wireless users in the same room can access the same spectrum band at the same time without interfering each other. Hence, the network capacity can be dramatically increased. To prove the feasibility of the proposed solution, an experimental testbed is first developed and evaluated. Then, the effects of the reflect-array on transport capacity of the indoor wireless networks are investigated. Through experiments, theoretical deduction, and simulations, this paper demonstrates that significantly higher spectrum-spatial efficiency can be achieved by using the smart reflect-array without any modification of the hardware and software in the users' devices.

The K L1-norm Principal Components (L1-PCs) of a data matrix X ∈ ℝD × Ncan be found optimally with cost O(2NK), in the general case, and O(Nrank(X)K - K + 1), when rankX is a constant with respect to N [1],[2]. Certainly, in real-world applications where N is large, even the latter polynomial cost is prohibitive. In this work, we present L1-BF: a novel, near-optimal algorithm that calculates the K L1-PCs of X with cost O (NDmin{N, D} + N2(K4+ DK2) + DNK3), comparable to that of standard (L2-norm) Principal-Component Analysis. Our numerical studies illustrate that the proposed algorithm attains optimality with very high frequency while, at the same time, it outperforms on the L1-PCA metric any counterpart of comparable computational cost. The outlier-resistance of the L1-PCs calculated by L1-BF is documented with experiments on dimensionality reduction and genomic data classification for disease diagnosis.

In the past decade, there has been a growing documented effort to approximate a matrix by another of lower rank minimizing the L1-norm of the residual matrix. In this paper, we first show that the problem is NP-hard. Then, we introduce a theorem on the sparsity of the residual matrix. The theorem sets the foundation for a novel algorithm that outperforms all existing counterparts in the L1-norm error minimization metric and exhibits high outlier resistance in comparison to usual L2-norm error minimization in machine learning applications.

We consider the problem of foreground and background extraction from compressed-sensed (CS) surveillance videos that are captured by a static CS camera. We propose, for the first time in the literature, a principal component analysis (PCA) approach that computes directly in the CS domain the low-rank subspace of the background scene. Rather than computing the conventionalL2-norm-based principal components, which are simply the dominant left singular vectors of the CS-domain data matrix, we compute the principal components under anL1-norm maximization criterion. The background scene is then obtained by projecting the CS measurement vector onto theL1principal components followed by total-variation (TV) minimization image recovery. The proposedL1-norm procedure directly carries out low-rank background representation without reconstructing the video sequence and, at the same time, exhibits significant robustness against outliers in CS measurements compared toL2-norm PCA. An adaptive CS-L1-PCA method is also developed for low-latency video surveillance. Extensive experimental studies described in this paper illustrate and support the theoretical developments.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access (which we also refer to as cognitive spectrum access) networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign: 1) portions of the spectrum and 2) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this paper, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems: 1) spectrum management through power allocation with given relay selection strategy; and 2) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a newly designed centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution.

Differential amplify-and-forward (DAF) cooperative relaying appears to be an attractive strategy for wireless networks where channel estimation is not feasible or it is rather avoided. In this work, we intend to determine the optimum relay location in order to minimize the outage probability of the DAF cooperative relaying in case that the system is allowed to deploy the relay. We consider two scenarios. First, we optimize the relay location for the DAF cooperative relaying with any given power assignment at the source and the relay. Second, we further consider relay location optimization with optimum power assignment in which the performance of the DAF relaying is further enhanced with the price of higher complexity. In order to obtain the optimum relay location, we develop a recursive algorithm based on the fix-point theorem and show that the relay location has a unique optimum solution which can be determined recursively from the sequence developed in our theorem. We also study the impact of the path-loss exponent to the optimal relay location and found that the relay should be deployed closer to the source in lossy environment such as near-ground environment than that in the free space environment. Extensive numerical results are provided to validate and illustrate the theoretical development.

In this paper we design, implement, and experimentally evaluate a wireless software-defined radio platform for cognitive channelization in the presence of narrowband or wideband primary stations. Cognitive channelization is achieved by jointly optimizing the transmission power and the waveform channel of the secondary users. The process of joint resource allocation requires no a-priori knowledge of the transmission characteristics of the primary user and maximizes the signal-to- interference-plus-noise ratio (SINR) at the output of the secondary receiver. This is achieved by designing waveforms that span the whole continuum of available/device-accessible spectrum, while satisfying a peak power constraint for the secondary users and an interference temperature (IT) constraint for the primary users. We build a four-node software-defined radio testbed and experimentally demonstrate in an indoor laboratory environment the theoretical concepts of all-spectrum cognitive channelization in terms of pre-detection SINR and bit-error-rate (BER) at both primary and secondary receivers.

We address the problem of recovering an unknown image of interest, when only few, severely corrupted copies are available. We employ, for the first time in the literature, corruption-resistant L1-Principal-Components (L1-PCs) of the image data-set at hand. Specifically, the calculated L1-PCs are used for reliability-based patch-by-patch fusion of the corrupted image copies into a single high-quality representation of the original image (L1-fusion). Our experimental studies illustrate that the proposed method offers remarkable recovery results for several common corruption types, even under high corruption rate, small number of copies, and varying corruption type among copies. An additional theoretical contribution of this work is that the L1-PC of a data matrix of non-negative entries (e.g., image data) is for the first time shown to be optimally calculable with complexity linear to the matrix dimensions - as of now, the fastest-known optimal algorithm is of polynomial complexity. In the light of this result, L1-fusion is carried out with linear cost comparable to that of the simple copy-averaging alternative. The linear-low cost of L1-fusion allows for the recovered image to be, optionally, further refined by means of sophisticated single-image restoration techniques.

In this work, we intend to carry out optimized spread-spectrum concealment of multiuser data under a given digital image. First, the overall image is pre-processed into transform-domain small blocks from which host vectors are obtained via zig-zag scanning vectorization. Multiuser data hiding is performed in the generated host vectors. Under this data hiding system model, we give an orthogonal set of embedding spread-spectrum signatures that achieves maximum sum signal-to-interference-plus-noise ratio at the output of the linear-filter receivers for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we present the optimal multi-signature assignment and amplitude allocation that maximizes the sum capacity of the concealment procedure. The practical implication of the results is sum signal-to-interference-plus-noise ratio, sum-capacity optimal multiuser/multi-signature spread-spectrum data hiding in the digital image medium. Numerical results demonstrate the effectiveness of the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we intend to hide multiuser data in a given host video stream with imperceptible spread-spectrum embedding. First, host video frames are picked in the original given video stream according to a frame selection pattern. We partition each host video frame into many small blocks. Based on a two-dimensional transformation of each small block and zigzag scanning, we construct the host video vectors. The embedded message for each user will be spread out with a signature and added to the host vectors. We present the orthonormal signature set of embedding carriers that achieves maximum sum signal-to-interference-plus-noise ratio (sum-SINR) at the linear filter output for any fixed embedding amplitudes. Then, for any given total embedding distortion constraint, we give the optimal multi-signature assignment and amplitude allocation pair that maximizes the sum capacity of the concealment procedure. Numerical results demonstrate the effectiveness of the proposed optimal data embedding methods in video streams.

Recently, Markopoulos et al. [1], [2] presented an optimal algorithm that computes the L1maximum-projection principal component of any set of N real-valued data vectors of dimension D with complexity polynomial in N, O(ND). Still, moderate to high values of the data dimension D and/or data record size N may render the optimal algorithm unsuitable for practical implementation due to its exponential in D complexity. In this paper, we present for the first time in the literature a fast greedy single-bit-flipping conditionally optimal iterative algorithm for the computation of the L1principal component with complexity O(N3). Detailed numerical studies are carried out demonstrating the effectiveness of the developed algorithm with applications to the general field of data dimensionality reduction and direction-of-arrival estimation.

We consider arbitrary Hybrid-Automatic-Repeat-Request (H-ARQ) wireless links over quasi-static Rayleigh fading channels. In this paper, we translate the repeat-request advantage of the intended receiver over potential eavesdroppers to link security. In particular, with statistical-only knowledge of the channel and noise, we find for the first time in the literature the optimal power allocation sequence over the H-ARQ rounds that maximizes the outage probability of eavesdroppers for any given target outage probability of the trusted receiver. Simulation studies demonstrate orders of magnitude difference in outage probability between eavesdroppers and intended receiver.

We investigate the problem of embedding data in raw video sequences with minimum video mean-square distortion for any required data recovery error rate. In particular, for any given video frame sequence and any (block) transform domain of interest, we find the optimal carrier and scalar parametrized linear operator on the video data that maximize the output signal-to-interference-plus-noise ratio (SINR) of the maximum-SINR data receiver filter or, equivalently, minimize the average embedding distortion for any target message extraction error rate. The procedure is extended from single-carrier to multi-carrier (multiple messages) embedding. As a practical consideration, a sub-optimal computationally efficient embedding algorithm is also proposed. Extensive experimental results demonstrate that sub-optimal embedding as described has video distortion versus data extraction error rate performance comparable to optimal embedding. Our studies also demonstrate the robustness of the optimal (and sub-optimal) embedding schemes to H.264 compliant encoding.

We consider the problem of embedding multiuser data in digital media (such as images, video sequences, audio) with minimum perceived distortion. In this direction, we find the orthonormal set of embedding carriers that achieves maximum sum signal-to-interference-plus-noise ratio (sum-SINR) at the output of the receiver linear filters for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we calculate the optimal multicarrier assignment and embedding amplitude values that maximize the sum capacity of the embedding process. Experimental results presented herein for multiuser data embedding in images demonstrate the effectiveness of the proposed methods.

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.

Pseudo-noise (PN) masking is regarded as an effective means to combat data eavesdropping (for example in military-grade communications or positioning systems). At the same time, PN-masked data transmissions are considered vulnerable to interference/jamming due to lack of practical interference suppression solutions. In this work, (i) we derive an efficient minimum-mean-square-error (MMSE) optimal linear receiver of PN-masked data and (ii) develop an auxiliary-vector (AV) MMSE adaptive filter estimator with state-of-the-art small-sample-support estimation performance. Simulation studies included in this paper illustrate the effectiveness of the theoretical developments.

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 new 4 × 4 Hadamard-precoded quasi-orthogonal space-time block code (QO-STBC) that enables highly effective near-maximum-likelihood (near-ML) reliability-based prioritized symbol detection using linear filters. Approximate block-error-rate minimization is being used to optimize the code rotation angle. Detailed computational complexity evaluation of the decoder in terms of real multiplications and additions shows significant complexity reduction for symbol alphabet sizes of interest. Numerical and simulation studies demonstrate negligible bit-error-rate degradation compared to the state-of-the-art in bit-error-rate by ML decoded 4 × 4 codewords.

In the context of secure wireless communications, pseudo-noise (PN) masking of transferred data has proven to be an effective technique against eavesdropping (notable examples are military-grade communication and global-positioning systems). At the same time, PN-masked transmissions are thought to be vulnerable to interference/jamming due to lack of a minimum-mean-square-error (MMSE) disturbance suppressing solution. In this paper, for the first time we establish the MMSE operation for masked data in the form of a time (mask) varying linear filter, suggest an implementation that avoids repeated input autocorrelation matrix inversion, and develop an auxiliary-vector (AV) MMSE filter estimator with state-of-the-art short-data-record estimation performance. Simulation examples included herein illustrate the theoretical developments.

Compressed sensing is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, in particular, we consider a video system where acquisition is carried out in the form of direct compressive sampling (CS) with no other form of sophisticated encoding. Therefore, the burden of quality video sequence reconstruction falls solely on the receiver side. We show that effective implicit motion estimation and decoding can be carried out at the receiver or decoder side via sparsity-aware recovery. The receiver performs sliding-window interframe decoding that adaptively estimates Karhunen–Loève bases from adjacent previously reconstructed frames to enhance the sparse representation of each video frame block, such that the overall reconstruction quality is improved at any given fixed CS rate. Experimental results included in this paper illustrate the presented developments.

We consider the problem of extracting blindly data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We develop a novel multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multicarrier spread-spectrum embedding. Neither the original host nor the embedding carriers are assumed available. Experimental studies on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.

We consider a secondary multi-hop cognitive radio network with decentralized control that operates cognitively to coexist with primary users. We propose a new spread-spectrum management paradigm, in which, unlike mainstream dynamic spectrum access research, digital waveforms are designed to occupy the entire available spectrum, and to adaptively track the interference profile at the receiver to maximize the link capacity while avoiding interference to primary users. In this context, we study the problem of maximizing the network throughput of a multi-hop network through joint routing and spread-spectrum channelization. We first propose a centralized formulation of the network control problem. We then propose an algorithm that can be seen as a distributed localized approximation of the throughput-maximizing policy. We refer to the proposed jointly-designed routing and code-division channelization algorithm as ROCH (Routing and cOde-division CHannelization). Specifically, power and spreading code are jointly selected to maximize the pre-detection secondary \mathrm{SINR} while providing quality of service guarantees to 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 study the throughput and delay performance of ROCH through a extensive simulation experiments, which demonstrate the appeal of the proposed framework through significant performance gains compared to baseline solutions.

In this paper, the optimal power assignment strategy is determined for hybrid automatic-repeat-request (H-ARQ) protocols such that the average delay of the protocol is minimized for any given total transmission power budget and any targeted outage probability. A set of equations is derived that describe the optimal transmission power sequence and its optimality is shown based on the Karush-Kuhn-Tucker (KKT) Theorem. The set of equations enables an exact recursive calculation of the optimal transmission power per round, and the calculation complexity is fixed regardless of the maximum number of (re)transmission rounds allowed in the H-ARQ protocol. Compared to the conventional equal power assignment strategy, the optimal power assignment scheme achieves the same average delay with much less total transmission power. More importantly, for certain power budget levels, the optimal power assignment can make the H-ARQ protocol work while the equal power assignment cannot. Extensive numerical results are presented to illustrate the theoretical development.

This paper considers the problem of blindly extracting data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We first develop a multi-signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding signatures are assumed available. Then, cross-correlation enhanced M-IGLS (CC-M-IGLS), a procedure described herein in detail that is based on statistical analysis of repeated independent M-IGLS processing of the host, is seen to offer most effective hidden message recovery. Experimental studies on images show that the proposed CC-M-IGLS algorithm can achieve recovery probability of error close to what may be attained with known embedding signatures and host autocorrelation matrix.

We consider the problem of designing binary antipodal uniquely decodable (errorless) code sets for overloaded code-division multiplexing applications where the number of signals K is larger than the code length L. Our proposed errorless code set design aims at identifying the maximum number of columns that can be potentially appended to a Sylvester-Hadamard matrix of order L, while maintaining the errorless code property. In particular, we derive formally the maximum number of columns that may be appended to the Sylvester-Hadamard matrix of order L = 8 and use this result as a seed to produce an infinite sequence of designs in increasing L. In the noiseless transmission case, a simple algorithm is developed to uniquely decode all signals. In additive white Gaussian noise (AWGN), a slab-sphere decoding scheme can be utilized for efficient and effective decoding.

We present a new 4×4 Hadamard-precoded Quasi-Orthogonal Space-Time Block Code (QO-STBC) that enables reliability-based prioritized symbol detection using linear filters. Approximate block-error-rate analysis is carried out and used to optimize the code rotation angles. For benchmarking purposes, the maximum-likelihood (ML) code detector is also derived with complexity of the order of joint two-real-symbol decoding. Numerical and simulation studies compare linear filter decoding against ML decoding.

In code-division multiplexing, the real/complex-valued sequence (signature) that maximizes the pre-detection signal-to-interference-plus-noise ratio (SINR) at the output of the maximum-SINR filter is simply the minimum-eigenvalue eigenvector of the disturbance autocorrelation matrix. SINR sequence optimization over a finite alphabet, however, is NP-hard in the sequence length. In this paper, we derive new easy to calculate upper and lower bounds on the SINR of the optimal binary and quaternary sequences that can serve as benchmarking tools for any suggested suboptimal designs.

For the general correlated multiple-access vector channel, we derive algebraically the sum-SINR (signal-to-interference-plus-noise ratio) optimal orthonormal set of carriers for any given set of carrier amplitudes. If, in addition, the correlated multiple-access channel is assumed Gaussian, the sum-capacity optimal orthonormal carrier set design and the jointly sum-capacity optimal orthonormal carrier and power assignment, for a given power budget, follow readily.

In this work, we consider the problem of assigning optimal transmission power sequence for cooperative hybrid automatic-repeat-request (H-ARQ) relaying protocol over quasi-static Rayleigh fading channels. We try to determine the optimal power sequence by minimizing the average total transmission power that we analyzed in our previous work. However, the closed-form expression of the average total power consumption of the cooperative H-ARQ relaying protocol is complicated in general, so we develop first in this work a simple approximation of the average total transmission power that is tight at high SNR. Then, based on the asymptotically tight approximation, we are able to identify the sequence of power values that minimizes the average total power consumption of the cooperative H-ARQ relaying protocol for any given targeted outage probability. In particular, we derive a set of equations that describe the optimal power level in each (re)transmission and enable its recursive calculation with fixed searching complexity. When the maximum number of (re)transmissions allowed in the protocol is L = 2, we have a closed-form result for the optimal transmission power sequence. The optimal power assignment solution reveals that conventional equal power assignment scheme is not optimal in general. Extensive simulation and numerical results are provided to illustrate and validate the theoretical results.

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.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign (i) portions of the spectrum and (ii) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate.

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.

We address the fundamental problem of identifying the optimal power assignment sequence for hybrid automatic-repeat-request (H-ARQ) communications over quasi-static Rayleigh fading channels. For any targeted H-ARQ link outage probability, we find the sequence of power values that minimizes the average total expended transmission power. We first derive a set of equations that describe the optimal transmission power assignment and enable its exact recursive calculation. To reduce calculation complexity, we also develop an approximation to the optimal power sequence that is close to the numerically calculated exact result. The newly founded power allocation solution reveals that conventional equal-power H-ARQ assignment is far from optimal. For example, for targeted outage probability of 10-3with a maximum of two transmissions, the average total transmission power with the optimal assignment is 9 dB lower than the equal-power protocol. The difference in average total power cost grows further when the number of allowable retransmissions increases (for example, 11 dB gain with a cap of 5 transmissions) or the targeted outage probability decreases (27 dB gain with outage probability 10-5and transmissions capped at 5). Interestingly, the optimal transmission power assignment sequence is neither increasing nor decreasing; its form depends on given total power budget and targeted outage performance levels. Extensive numerical and simulation results are presented to illustrate the theoretical development.

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. Our objective is to find the optimum transmitting power and code sequence of the secondary channel that maximize the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear receiver, while at the same time the SINR of all primary channels at the output of their max-SINR receiver is maintained above a certain threshold. This is a non-convex NP-hard optimization problem. We propose a novel feasible suboptimum solution using semidefinite programming. Simulation studies illustrate the theoretical developments.

We consider the problem of cognitive code-division channelization (simultaneous power and code-channel allocation) for secondary transmission links co-existing with an unknown primary code-division multiple-access (CDMA) system. We first develop a blind primary-user identification scheme to detect the binary code sequences (signatures) utilized by primary users. To create a secondary link we propose two alternative procedures -one of moderate and one of low computational complexity- that optimize the secondary transmitting power and binary code-channel assignment in accordance with the detected primary code channels to avoid "harmful" interference. At the same time, the optimization procedures guarantee that the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear secondary receiver is no less than a certain threshold to meet secondary transmission quality of service (QoS) requirements. The extension of the channelization problem to multiple secondary links is also investigated. Simulation studies presented herein illustrate the theoretical developments.

In this paper, we present the lowest-computational-complexity maximum-likelihood (ML) decoder known to-date for 4×4 full-diversity Quasi-Orthogonal Space-Time Block Codes (QO-STBC) with symbols from square or rectangular quadrature amplitude modulation (QAM) constellations. The complexity savings come from a simplified quadratic ML decoding statistic that is being presented and the utilization of the signal points of the QAM constellation. Comparative computational complexity analysis is carried out and a simulation study demonstrates the theoretical equivalence of the proposed and original ML implementation.

In this paper, the average total power consumption per information packet is investigated for a cooperative hybrid automatic-repeat-request (H-ARQ) protocol in a quasistatic Rayleigh fading environment. Specifically, a closed-form expression of the average total transmission power is obtained for the cooperative H-ARQ relay protocol, in which the source may use different transmission power level in different (re-)transmission rounds. The closed-form expression is valid for any maximum number of (re-)transmission rounds L allowed by the protocol and may play a key role thereafter in optimizing power allocation. Since the closed-form expression is complicated for large L, an approximation of the average total transmission power is developed which is asymptotically tight at high SNR. Extensive simulation and numerical results are also provided herein to illustrate and validate the theoretical results.

Differential Amplify-and-Forward (DAF) relaying can be viewed as an attractive cooperative communication strategy for wireless networks where channel estimation is not feasible or it is rather avoided. A new exact outage probability expression is presented herein for DAF relaying that involves only a single integral. Then, an asymptotically tight closed-form approximation is obtained which enables power allocation optimization for the outage behavior of DAF relaying. Numerical and simulation studies validate the theoretical analysis.

We address the fundamental problem of identifying the optimal power allocation sequence for hybrid automatic-repeat-request (H-ARQ) communications over quasistatic Rayleigh fading channels. For any targeted H-ARQ link outage probability, we find the sequence of power values that minimizes the average total expended transmission power. The newly founded power allocation solution reveals that conventional equal-power H-ARQ assignment is far from optimal. For example, for targeted outage probability of 10-3with a maximum of two transmissions, the average total transmission power with optimal assignment is 9dB lower than the equal-power protocol. The difference in average total power cost grows further when the number of allowable retransmissions increases (for example, lldB gain with a cap of 5 transmissions) or the targeted outage probability decreases (27dB gain with outage probability 10-5and transmissions capped at 5).

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 through put maximization 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.

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/complexfield sets shows that, arguably, not much is to be gained by raising the alphabet size above four for any K, L.

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. The sum-capacity (as well as the maximum squared correlation and total asymptotic efficiency) of minimum TSC quaternary sets is also evaluated in closed-form and contrasted against the sum capacity of minimum-TSC optimal binary and real/complex sets.

An 8times8 two-symbol decodable quasi-orthogonal space-time block code (QO-STBC) is presented which can be transmitted across either 8 or 4 antennas with full rate and the same full diversity order. For the 8-transmit-antenna system, a new expression is developed to identify rotation angles that maximize the diversity (eigenvalue) product. In addition, it is shown that the previously proposed sum-eigenvalue maximization criterion for the design of rotation angles is not relevant/applicable and an alternative minimum eigenvalue maximization criterion is suggested. Finally, new optimal rotation angles are obtained by working directly with a pairwise-error-probability (PEP) upperbound expression. For 4-transmit-antenna systems and correlated channel fading conditions, the PEP-upper-bound is modified accordingly to take into account the channel correlation. Using the new PEP-upper-bound we obtain rotation angles that maximize the diversity product and find, contrary to previous results, that the optimized angles are independent of the correlation coefficient. Simulation studies initiated herein demonstrate the advantage of using the proposed codeword across 4 transmit antennas when compared with other 4times4 QO-STBC transmission schemes. For 8 transmit antennas, the studies compare the three selected rotation angle optimization criteria (diversity product, minimum eigenvalue, PEP-upper-bound).

When data symbols modulate a signature waveform to move across a channel in the presence of disturbance, the signature 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 the signature alphabet is finite and digital signature optimization is NP-hard. In this paper, we present a formal search procedure of cost, upon eigenvector decomposition, log-linear in the signature code length that returns the maximum-SINR binary signature vector near arcs of least SINR decrease from the real maximum SINR solution in the Euclidean vector space. The quality of the proposed adaptive binary designs is measured against the theoretical upper bound of the complex/real eigenvector maximizer.

We derive a new expression for the rotation angles that maximize the diversity (eigenvalue) product of the 8×8 two-symbol decodable quasi-orthogonal space-time block code (QO-STBC). We show that the previously proposed sum-eigenvalue maximization criterion for the design of rotation angles is not relevant/applicable to the 8-transmit-antenna QO-STBCs and we suggest, instead, minimum eigenvalue maximization. Finally, working directly with the pairwise-error-probability (PEP) upper bound expression, we obtain new true PEP-upper-bound optimal rotation angles. Simulation studies demonstrate and compare the error rate of the three design criteria (diversity product, minimum eigenvalue, PEP upper bound).

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

For any given host image or group of host images and any (block) transform domain of interest, we find the signature vector that when used for spread-spectrum (SS) message embedding maximizes the signal-to-interference-plus-noise ratio (SINR) at the output of the corresponding maximum-SINR linear filter. We establish that, under a (colored) Gaussian assumption on the transform domain host data, the same derived signature minimizes host distortion for any target message recovery error rate and maximizes the Shannon capacity of the covert steganographic link. Then, we derive jointly optimal signature and linear processor designs for SS embedding in linearly modified transform domain host data and demonstrate orders of magnitude improvement over current SS steganographic practices. Optimized multisignature/multimessage embedding in the same host data is studied as well.

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.

Recent advances in the area of binary sequences for code-division multiplexing provide us with minimum total-squared-correlation (TSC) optimal signature sets for (almost) all signature lengths L and set sizes K. The sets are scalable as long as K les L (underloaded systems) and non- scalable -in general require signature re-design/re-assignment as users enter or exit- when K > L (overloaded systems). In this paper, we derive new lower bounds on the conditional TSC of overloaded binary signature sets built on fixed full- load TSC-optimal sets. Overloading is allowed to be as high as 100%. Scalable designs that achieve the new bounds are then developed. To evaluate the performance of the proposed designs, we compare the TSC of our constructions to the unconditionally minimum achievable TSC values.

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 correspondence, we examine the relationship between Dempster-Shafer (DS) decision making systems and conventional Bayesian (centralized or distributed) detectors. We establish theoretically that under certain intuitively pleasing DS probability mass assignments the two systems become equivalent. A few numerical studies illustrate the theoretical developments.

We use the theory of finite classical generalized polygons to derive and study low-density parity-check (LDPC) codes. The Tanner graph of a generalized polygon LDPC code is highly symmetric, inherits the diameter size of the parent generalized polygon, and has minimum (one half) diameter-to-girth ratio. We show formally that when the diameter is four or six or eight all codewords have even Hamming weight. When the generalized polygon has in addition equal number of points and lines, we see that the non-regular polygon based code construction has minimum distance that is higher at least by two in comparison with the dual regular polygon code of the same rate and length. A new minimum distance bound is presented for codes from non-regular polygons of even diameter and equal number of points and lines. Finally, we prove that all codes derived from finite classical generalized quadrangles are quasi-cyclic and we give the explicit size of the circulant blocks in the parity check matrix. Our simulation studies of several generalized polygon LDPC codes demonstrate powerful bit-error-rate performance when decoding is carried out via low complexity variants of belief propagation.

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.

In this paper, we develop a new low-complexity algorithm to decode low-density parity-check (LDPC) codes. The developments are oriented specifically toward low-cost, yet effective, decoding of (high-rate) finite-geometry (FG) LDPC codes. The decoding procedure updates iteratively the hard-decision received vector in search of a valid codeword in the vector space. Only one bit is changed in each iteration, and the bit-selection criterion combines the number of failed checks and the reliability of the received bits. Prior knowledge of the signal amplitude and noise power is not required. An optional mechanism to avoid infinite loops in the search is also proposed. Our studies show that the algorithm achieves an appealing tradeoff between performance and complexity for FG-LDPC codes.

In 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. The layers may be time-multiplexed and transmitted over a single CDMA channel or each layer can be transmitted over a different CDMA channel. For the latter case, spreading codes of 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 signal-to-noise-ratio scalable bit stream. Each of the two layers is channel-coded using rate-compatible punctured convolutional codes. Then, the data are interleaved, spread, carrier-modulated, and transmitted over the wireless channel. A multipath Rayleigh fading channel model is assumed. At the other end, the signal is collected by an antenna array front. After carrier demodulation, multiple-access-interference suppressing despreading is performed using adaptive space-time auxiliary-vector (AV) filters. The choice of the AV space-time receiver is dictated by realistic channel fading rates that limit the data record available for receiver adaptation and redesign. Our experimental results demonstrate the effectiveness of such a multirate DS-CDMA system for wireless video transmission.

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 use the theory of finite classical generalized polygons to derive and study low-density parity-check (LDPC) codes. The Tanner graph of a generalized polygon LDPC code is highly symmetric, inherits the diameter size of the parent generalized polygon, and has minimum (one half) diameter-to-girth ratio. We show formally that when the diameter is four or six or eight, all codewords have even Hamming weight. When the generalized polygon has in addition an equal number of points and lines, we see that the nonregular polygon based code construction has minimum distance that is higher at least by two in comparison with the dual regular polygon code of the same rate and length. A new minimum-distance bound is presented for codes from nonregular polygons of even diameter and equal number of points and lines. Finally, we prove that all codes derived from finite classical generalized quadrangles are quasi-cyclic and we give the explicit size of the circulant blocks in the parity-check matrix. Our simulation studies of several generalized polygon LDPC codes demonstrate powerful bit-error-rate (BER) performance when decoding is carried out via low-complexity variants of belief propagation.

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.

In this paper, we characterize analytically the multiaccess interference (MAI) in wireless CDMA sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of the signature code length and the receiver design on network performance for different topologies is also studied.

In this paper, we characterize analytically the multiaccess interference in wireless code-division multiple-access sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of signature code length and receiver design on the network performance for different topologies is also studied.

In this letter, we report results on the relative performance of scalable video transmission via a single-rate or a multirate direct sequence code-division multiple-access channel using minimum total squared correlation spreading codes. Our findings demonstrate the superiority of the multirate system on a wide range of chip rates of practical interest.

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.

We develop a new low complexity algorithm for decoding low-density parity-check (LDPC) codes. The developments are oriented specifically toward the low cost-yet effective-decoding of (high rate) finite geometry LDPC codes. The decoding procedure updates the hard-decision received vector iteratively in search of a valid codeword in the vector space. Only one bit is changed in each iteration and the bit selection criterion combines the number of failed checks and the reliability of the received bits. Prior knowledge of the signal amplitude and noise power is not required. An optimal mechanism to avoid infinite loops in the search is also proposed. Our studies show that the algorithm achieves an appealing performance versus complexity trade-off for finite geometry LDPC codes.

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.

The prohibitive - exponential in the number of users - computational complexity of the maximum-likelihood multiuser detector for direct-sequence code-division multiple-access communications has fueled an extensive research effort for the development of low-complexity multiuser detection alternatives. We show that we can efficiently and effectively approach the error rate performance of the optimum multiuser detector as follows. We utilize a multiuser zero-forcing or minimum mean-square error (MMSE) linear filter as a preprocessor and we establish that the output magnitudes, when properly scaled, provide a reliability measure for each user bit decision. Then, we prepare an ordered, reliability-based error search sequence of length linear in the number of users; it returns the most likely user bit vector among all visited options. Numerical and simulation studies for moderately loaded systems that permit exact implementation of the optimum detector indicate that the error rate performance of the optimum and the proposed detector are nearly indistinguishable over the whole predetection. signal-to-noise ratio range of practical interest. Similar studies for higher user loads (that prohibit comparisons with the optimum detector) demonstrate error rate performance gains of orders of magnitude in comparison with straight decorrelating or MMSE multiuser detection.

Two distinct codeword-searching procedures based on iterative bounded-distance decoding (BDD) are combined to form an adaptive two-stage maximum-likelihood (ML) decoder for binary linear block codes. During the first stage of the algorithm, a tight upper bound on an error likelihood metric ("discrepancy") is established iteratively for the ML codeword. First-stage processing requires sorting and storage. Adaptive switching to the second stage removes the sorting and storage requirements and allows to rule out redundant BDDs efficiently. Second-stage processing accounts for all codewords with discrepancy lower bound below the upper bound of the ML codeword and guarantees ML performance. In addition, the proposed two-stage algorithm is inherently tunable for controlled suboptimum operation. Under sub-ML operation, the overall scheme can be interpreted as a generalization of the Chase (1972) algorithm. Simulation studies for the (24,12,8) extended Golay and the (64,30,14) and (128,64,22) extended Bose-Chaudhuri-Hocquenghem (BCH) codes illustrate and support these theoretical developments.

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 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 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.

The problem under consideration is the adaptive reception of a multipath direct-sequence spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive impulsive noise. An SS receiver structure is proposed that consists of a vector of adaptive chip-based Hampel nonlinearities followed by an adaptive auxiliary-vector linear tap-weight filter. The nonlinear receiver front end adapts itself to the unknown prevailing noise environment providing robust performance over a wide range of underlying noise distributions. The adaptive auxiliary-vector linear tap-weight filter allows rapid SS interference suppression with a limited data record. Numerical and simulation studies under finite-data-record system adaptation show significant improvement in bit-error-rate performance over the conventional linear minimum variance-distortionless-response (MVDR) SS receiver or conventional MVDR filtering preceded by vector adaptive chip-based nonlinear processing.

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.

In contrast to conventional feedback bidirectional associative Memory (BAM) network models, a feedforward BAM network is developed based on a one-shot design algorithm of O(p(2)(n+m)) computational complexity, where p is the number of prototype pairs and n, m are the dimensions of the input/output bipolar vectors. The feedforward BAM is an n-p-m three-layer network of McCulloch-Pitts neurons with storage capacity 2(min{m,n}) and guaranteed perfect bidirectional recall. The overall network design procedure is fully scalable in the sense that any number p= or <2(min{m,n}) of bidirectional associations can be implemented. The prototype patterns may be arbitrarily correlated. With respect to inference performance, it is shown that the Hamming attractive radius of each prototype reaches the maximum possible value. Simulation studies and comparisons illustrate and support these theoretical developments.

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.

Receiver architectures in the form of a linear filter front-end followed by a hard-limiting decision maker are considered for DS-CDMA communication systems. Based on stochastic approximation concepts a recursive algorithm is developed for the adaptive optimization of the linear filter front-end in the minimum BER sense. The recursive form is decision driven and distribution free. For additive white Gaussian noise (AWGN) channels, theoretical analysis of the BER surface of linear filter receivers identifies the subset of the linear filter space where the optimal receiver lies and offers a formal proof of guaranteed global optimization with probability one for the two-user case. To the extent that the output of a linear DS-CDMA filter can be approximated by a Gaussian random variable, a minimum-mean-square-error optimized linear filter approximates the minimum BER solution. Numerical and simulation results indicate that for realistic AWGN DS-CDMA systems with reasonably low signature cross-correlations the linear minimum BER filter and the MMSE filter exhibit approximately the same performance. The linear minimum BER receiver is superior, however, when either the signature cross-correlation is high or the background noise is non-Gaussian.

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.

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).

Hypothesis testing is a collective name for problems such as classification, detection, and pattern recognition. In this paper we propose two new classes of supervised learning algorithms for feedforward, binary-output neural network structures whose objective is hypothesis testing. All the algorithms are applications of stochastic approximation and are guaranteed to provide optimization with probability one. The first class of algorithms follows the Neyman-Pearson approach and maximizes the probability of detection, subject to a given false alarm constraint. These algorithms produce layer-by-layer optimal Neyman-Pearson designs. The second class of algorithms minimizes the probability of error and leads to layer-by-layer Bayes optimal designs. Deviating from the layer-by-layer optimization assumption, we propose more powerful learning techniques which unify, in some sense, the already existing algorithms. The proposed algorithms were implemented and tested on a simulated hypothesis testing problem. Backpropagation and perceptron learning were also included in the comparisons.

The problem of binary hypothesis testing is revisited in the context of distributed detection with feedback. Two basic distributed structures with decision feedback are considered. The first structure is the fusion center network, with decision feedback connections from the fusion center element to each one of the subordinate decisionmakers. The second structure consists of a set of detectors that are fully interconnected via decision feedback. Both structures are optimized in the Neyman-Pearson sense by optimizing each decision-maker individually. Then, the time evolution of the power of the tests is derived. Definite conclusions regarding the gain induced by the feedback process and direct comparisons between the two structures and the optimal centralized scheme are obtained through asymptotic studies (that is, assuming the presence of asymptotically many local detectors). The behavior of these structures is also examined in the presence of variations in the statistical description of the hypotheses. Specific robust designs are proposed and the benefits from robust operations are established. Numerical results provide additional support to the theoretical arguments.>

This paper considers the problem of Neyman-Pearson distributed detection. In distributed detection structures, a number of subordinate decision makers decide upon the active hypothesis based on their own data, and then transmit these decisions to one or more primary decision makers. Then the Neyman-Pearson performance criterion is deployed, the objective is to maximize the probability of detection (also known as power probability) induced by the primary decision makers, subject to a given false alarm constraint. In this formulation, the overall optimization problem reduces to the problem of threshold evaluation. This paper deals exactly with this issue. An on-line threshold learning algorithm is proposed that operates directly an data and requires-no explicit knowledge of the underlying probability distributions. The algorithm adapts recursively the pertinent threshold parameters in a way that minimizes the Kullback-Leibler distance between the observed and the desired output distribution. A formal convergence study is carried out and shows that, under some general conditions, the algorithm is strongly consistent; that is, the sequences of the produced threshold estimates converge to the optimal threshold values with probability 1. The rate of convergence is examined, and methods for controlling it are proposed. Simulation results are included and provide additional support to the theoretical arguments.>

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>