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 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.

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 design, implement, and evaluate an acoustic receiver structure for distributed multi-input and multi-output (MIMO) underwater systems that accounts for multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs) encountered in real deployments of underwater communication systems. We focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations. In this paper, we utilize distributed underwater sensors to form virtual MIMO underwater systems without requiring frequency or time synchronization. The proposed receiver consists of a bank of matched filters (one per effective CFO) at each receive antenna, followed by an information symbol detector. Each filter in the bank is sampled at the symbol rate with sampling timing selected according to the corresponding TO. We evaluate in real-time the performance of our algorithmic developments in a software-defined underwater testbed that utilizes in-house built software-defined acoustic modems (SDAMs). Experimental studies in both indoor, lab-controlled (tank) and outdoor (lake) real-world environments demonstrate superior bit-error-rate (BER) receiver performance compared to receiver designs that are not able to accommodate multiple CFOs and multiple TOs.

We propose a novel receiver design for distributed MIMO systems that accounts for multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs). The proposed structure utilizes a bank of pulse matched filters (one per effective CFO) at each receive antenna, followed by an information symbol detector. Each filter in the bank is sampled at the symbol rate with sampling timing selected according to the corresponding TO. For the proposed receiver configuration, we derive the maximum likelihood (ML) detector. Our theoretical developments are illustrated through extensive simulation studies and indicate that the proposed receiver structure together with the optimal ML detection offers significant performance gains compared to the current state of the art.

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.

Existing commercial wireless systems are mostly hardware-based, and rely on closed and inflexible designs and architectures. Moreover, despite recent significant algorithmic developments in cross-layer network adaptation and resource allocation, existing network architectures are unable to incorporate most of these advancements. While software-defined radio (SDR) was envisioned as a new paradigm promising radical runtime adaptation through all layers of the networking protocol stack, the reality of the state-of-the-art in wireless networking practice is far from having fulfilled such promise of fast and intelligent reconfigurability and adaptability. Networking research based on the “software-defined radio” paradigm has suffered almost invariably from the lack of adequate and coherently designed abstractions to (i) define networking protocols and their cross-layer interactions across all layers of the protocol stack; (ii) define decision-making algorithms to control such interactions. To address this need, we introduce RcUBe (Real-time Re-configurable Radio), a novel architectural radio framework based on abstractions that offer real-time reconfigurability and optimization capabilities at the PHY, MAC, and network layers of the protocol stack. Unlike state-of-the-art solutions, RcUBe offers a structured methodology at variable levels of abstraction to accommodate implementations of a wide range of network architectures and protocols and complex decision-making in a modular, platform-independent way. RcUBe provides these features through a design structured into four distinct, but interacting planes, namely decision, control, data, and register plane. The broad capabilities of the proposed framework are demonstrated on a network level software-defined radio setup through a range of experiments where RcUBe is used to implement various reconfigurable functionalities of a wireless system at the PHY, MAC, and network layer.

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.

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.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e. equal time duration is assigned to each source and each relay, in this work we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. We jointly optimize the power and time allocations for the cooperative relaying protocol such that the outage probability of the protocol is minimized. Specifically, for any given time allocation, we are able to determine the corresponding optimum power allocation analytically with a closed-form expression. We also show that in order to minimize the outage probability of the protocol, one should always allocate more energy and time to the source than the relay. Extensive numerical and simulation studies illustrate our theoretical developments.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e. equal time duration is assigned to each source and each relay, we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. In our recent work [18], we explored an ideal non-equal-time cooperative relaying protocol where the system can use arbitrary re-encoding methods at the relay and adjust time allocation arbitrarily, in which we showed that with optimum power and time allocation the cooperative relaying protocol has significant performance improvement compared to the equal-time relaying protocol. In this paper, with more realistic consideration, we design a practical cooperative relaying protocol based on linear mapping, i.e. using linear mapping as the re-encoding method at the relay and considering integer time slots in the two phases. Furthermore, we develop an optimum linear mapping to minimize the outage probability of the linear-mapping based cooperative relaying protocol. Numerical and simulation results show that the performance of the proposed cooperative relaying protocol based on the optimum linear mapping is close to the performance benchmark of the ideal cooperative protocol.

In this paper, we systematically analyze the outage probability performance of the distributed cooperative multicast with randomized orthogonal space-time codes (OSTC) for wireless multimedia networks. The distributed cooperative multicast protocol with randomized OSTC enables users who successfully decode signals from base station or access point serving as relays to forward the signals to other users, and consequently the overall performance of wireless multimedia multicast can be greatly improved. We derive a closed-form expression for the outage probability for the distributed cooperative multicast with randomized OSTC. To get more insight, we further develop an asymptotically tight approximation for the outage probability which reveals the diversity order of the protocol as K + 1, where K is the size of the randomized OSTC used in the protocol. Based on the asymptotically tight approximation of the outage probability, we are able to determine an optimum power allocation for the distributed cooperative multicast protocol. It shows that in a statistic sense, we should allocate 1/(K + 1) of the total power budget to the BS/AP and evenly distribute the rest for the users as relaying power. Numerical and simulation results validate our theoretical development.

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.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e., equal time duration is assigned to each source and each relay, in this work we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. We consider a cooperative relaying network in which no channel state information (CSI) is available at the transmitter side and the protocol optimization is based on channel statistics (i.e., mean and variance) and it does not depend on instantaneous channel information. First, we consider an ideal cooperative relaying protocol where the system can use arbitrary re-encoding methods at the relay and adjust time allocation arbitrarily. We obtain an optimum strategy of power and time allocations to minimize the outage probability of the ideal cooperative protocol. Specifically, for any given time allocation, we are able to determine the corresponding optimum power allocation analytically with a closed-form expression. We also show that to minimize the outage probability of the protocol, one should always allocate more energy and time to the source than the relay. Second, with more realistic consideration, we design a practical cooperative relaying protocol based on linear mapping, i.e., using linear mapping as the re-encoding method at the relay and considering integer time slots in the two phases. The theoretical results from the ideal cooperative protocol serve as a guideline and benchmark in the practical cooperative protocol design. We also develop an optimum linear mapping to minimize the outage probability of the linear-mapping based cooperative protocol. Extensive numerical and simulation studies illustrate our theoretical developments and show that the performance of the proposed cooperative relaying protocol based on the optimum linear mapping is close to the performance benchmark of the ideal cooperative protocol.

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.

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 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 capacity of airborne multiple-input-multiple-output (MIMO) wireless communication systems with arbitrary alignments of linear transmit and receive antenna arrays is systematically analyzed and the maximum achievable capacity is determined. Based on a general three-dimensional (3D) airborne MIMO communication model, we are able to approximate the airborne MIMO capacity as a function of the transmit and receive antenna array geometry in the 3D space. The capacity approximation is asymptotically tight as the distance between the transmit and receive antenna arrays large compared to their size. Based on the asymptotically tight capacity approximation, we derive an upper bound as well as a lower bound of the airborne MIMO capacity. Interestingly, both the upper and lower bounds are achievable. We also derive a necessary and sufficient condition for airborne MIMO communication systems to achieve the capacity upper bound for any given 3D transceiver antenna array geometry. The necessary and sufficient condition allows us to properly select the system parameters and design airborne MIMO communication systems that reach the best possible performance in terms of system capacity. We prove that when the distance between the transmit and receive antenna arrays is within a certain range, there exists a set of system parameter values (e.g. antenna element separation) for which the capacity of the MIMO communication system achieves the theoretical upper bound and this capacity value is larger than the average capacity of the corresponding conventional MIMO communication system under Rayleigh fading. Finally, we prove that the airborne MIMO capacity converges to the capacity lower bound when the distance between the transmit and receive antenna arrays goes to infinity. Extensive numerical studies included in this paper illustrate and validate our theoretical developments.

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

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

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.

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.

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

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

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

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

In this work, a cognitive cooperative communication protocol is proposed for cognitive ad-hoc networks, in which primary users and cognitive radio (CR) users may cooperate for mutual benefit. The new cooperation protocol allows active cooperation between primary users and CR users in which CR users assist to relay primary users' signals in exchange for some spectrum released from the primary users. While conventional cognitive radios do not guarantee continuous operation of CR users (they use the spectrum only when primary users do not), the protocol proposed in this work provides continuous service for CR users. The proposed cognitive cooperation protocol is optimized in terms of maximizing the primary user's energy savings and the CR user's own data transmission rate. It turns out that the primary users have significantly average energy savings from cooperation (e.g. up to 50% when compared to a non-cooperation case at the same transmission power level), which provides a good incentive for they to cooperate.

Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where the availability of local spectrum resources may change from time to time and hop-by-hop. Cooperative transmission exploits spatial diversity without multiple antennas at each node to increase capacity with reliability guarantees. This idea is particularly attractive in wireless environments due to the diverse channel quality and the limited energy and bandwidth resources. With cooperation, source node and relay node cooperatively transmit data to the destination. In such a virtual multiple antenna transmission system, the capacity of the cooperative link is much larger than that of the direct link from source to destination. In this paper, we will study decentralized and localized algorithms for joint dynamic routing, relay assignment, and spectrum allocation under a distributed and dynamic environment.

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.

In this paper we develop a new analytical methodology for the evaluation of the outage probability of cooperative decode-and-forward (DF) automatic-repeat-request (ARQ) relaying under packet-rate fading (fast fading or block fading) channels, where the channels remain fixed within each ARQ transmission round, but change independently from one round to another. We consider a single relay forwarding Alamouti-based retransmission signals in the cooperative ARQ scheme. In particular, (i) we derive a closed-form asymptotically tight (as SNR → ∞) approximation of the outage probability; (ii) we show that the diversity order of the DF cooperative ARQ relay scheme is equal to 2L-1, where L is the maximum number of ARQ (re)transmissions; and (iii) we develop the optimum power allocation for the DF cooperative ARQ relay scheme. The closed-form expression clearly shows that the achieved diversity is partially due to the DF cooperative relaying and partially due to the fast fading nature of the channels (temporal diversity). With respect to power allocation, it turns out that the proposed optimum allocation scheme depends only on the link quality of the channels related to the relay, and compared to the equal power allocation scheme it leads to SNR performance gains of more than 1 dB. Numerical and simulation studies illustrate the theoretical developments.

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

Throughput maximization is one of the main challenges in cognitive radio ad hoc networks, where local spectrum resources may change from time to time and hop-by-hop. For this reason, a cross-layer opportunistic spectrum access and dynamic routing algorithm for cognitive radio networks is proposed, called ROSA (ROuting and Spectrum Allocation algorithm). Through local control actions, ROSA aims at maximizing the network throughput by performing joint routing, dynamic spectrum allocation, scheduling, and transmit power control. Specifically, the algorithm dynamically allocates spectrum resources to maximize the capacity of links without generating harmful interference to other users while guaranteeing bounded BER for the receiver. In addition, the algorithm aims at maximizing the weighted sum of differential backlogs to stabilize the system by giving priority to higher-capacity links with high differential backlog. The proposed algorithm is distributed, computationally efficient, and with bounded BER guarantees. ROSA is shown through discrete-event packet-level simulations to outperform baseline solutions leading to a high throughput, low delay, and fair bandwidth allocation.

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.

In this paper we design novel generalized likelihood ratio test (GLRT)-type packet-data detectors for general multiaccess/multiuser digital communication systems and we develop analytical performance evaluation tools for finite data packet sizes. For the known channel case, we derive a coherent GLRT packet-data detector while for the unknown channel case we derive both a coherent pilot assisted GLRT packet-data detector and a differential phase-shift-keying (DPSK) GLRT packet-data detector. Efficient suboptimum implementations of the above schemes that exhibit complexity linear in the packet size are also considered. Simulation studies evaluate the performance of the proposed schemes in the context of packet-data code-division multiple access (CDMA) communications.

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 revisit the problem of recovering a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive Gaussian or impulsive noise. The general receiver structure is composed of an adaptive chip-based nonlinear pre-processor followed by an adaptive linear filter. We propose two blind algorithms for the adaptation of the parameters of the nonlinear pre-processor that are based on the Fisher discriminant function evaluated at the linear filter output. The first algorithm couples itself with the adaptation of the linear post-processor, while the second algorithm is a decoupled procedure that is independent of the linear post-processor. Both algorithms lead to universal receiver solutions in the sense that they mitigate a wide range of additive noise effects (from purely Gaussian to impulsive) and correlated SS interference. Both algorithms offer superior detection performance when receiver adaptation is carried out with limited input data observations

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

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

We 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 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.

In this paper we propose novel generalized likelihood ratio test (GLRT) packet-data detectors for general multiaccess/multiuser digital communication systems and we develop analytical performance evaluation tools for finite data packet sizes. We evaluate the performance of the proposed GLRT schemes in the context of packet-data CDMA communications.

The presence of the desired signal during estimation of the minimum mean-square error (MMSE)/minimum-variance distortionless-response (MVDR) and auxiliary-vector (AV) filters under limited data support leads to significant signal-to-interference-plus-noise ratio (SINR) performance degradation. We quantify this observation in the context of direct-sequence code-division multiple-access (DS-CDMA) communications by deriving close approximations for the mean-square filter estimation error, the probability density function of the output SINR, and the probability density function of the symbol-error rate (SER) of the sample matrix inversion (SMI) receiver evaluated using both a desired-signal-"present" and desired-signal-"absent" input covariance matrix. To avoid such performance degradation, we propose a DS-CDMA receiver that utilizes a simple pilot-assisted algorithm that estimates and then subtracts the desired signal component from the received signal prior to filter estimation. Then, to accommodate decision-directed operation, we develop two recursive algorithms for the on-line estimation of the AV and MMSE/MVDR filter and we study their convergence properties. Finally, simulation studies illustrate the SER performance of the overall receiver structures.

We investigate the data-record size requirements of sample-matrix-inversion-based minimum-variance-distortionless response and maximum-signal-to-interference-plus-noise-ratio adaptive algorithms to meet a given performance objective in joint space-time signal-detection problems for direct-sequence code-division multiple-access systems. We derive closed-form expressions that provide the data-record size that is necessary to achieve a given performance confidence level in a neighborhood of the optimal performance point, as well as expressions that identify the performance level that can be reached for a given data-record size. This is done by using close approximations of the involved probability density functions. The practical significance of the derived expressions lies in the fact that the expressions are functions of the number of antenna elements, the number of multipaths, and the system spreading gain only, while they depend neither on the ideal input covariance matrix, which is not known in most realistic applications, nor on the exact ideal performance value.

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

For pt.I see ibid., vol.51, p.983-94 (2003). We investigate the coarse synchronization performance of blind adaptive linear self-synchronized receivers for asynchronous direct-sequence code-division multiple-access communications under finite data record adaptation. Based on transformation noise modeling techniques, three alternative methods are developed, leading to analytical expressions that approximate the probability of coarse synchronization error of matched-filter-type and minimum-variance distortionless-response-type receivers. The expressions are explicit functions of the data record size and the filter order and reveal the effect of short data-record sample matrix-inversion implementations on the coarse synchronization performance. Besides their theoretical value, the derived expressions provide simple, highly-accurate alternatives to computationally demanding performance evaluation through simulations. The effect of the data record size on the probability of coarse synchronization error is further quantified through the use of a receiver synchronization resolution metric. Numerical and simulation studies examine the accuracy of the theoretical developments and show that the derived expressions approximate closely the actual coarse synchronization performance.

Blind adaptive linear receivers are considered for the demodulation of direct-sequence code-division multiple-access signals in asynchronous transmissions. The proposed structures are self-synchronized in the sense that adaptive synchronization and demodulation are viewed and treated as an integrated receiver operation. Two computationally efficient combined synchronization/demodulation schemes are proposed, developed, and analyzed. The first scheme is based on the principles of minimum-variance distortionless-response processing, while the second scheme follows the principles of auxiliary-vector filtering and exhibits enhanced performance in short data-record scenarios. In both cases, the resulting receiver is a linear structure of order exactly equal to the system processing gain. Simulation studies included in this paper demonstrate the coarse synchronization as well as the bit-error rate performance of the proposed strategies.

When the auxiliary vector (AV) filter generation algorithm utilizes sample average estimated input data statistics, it provides a sequence of estimates of the ideal minimum mean-square error or minimum-variance distortionless-response filter for the given signal processing/receiver design application. Evidently, early nonasymptotic elements of the sequence offer favorable bias/variance balance characteristics and outperform in mean-square filter estimation error the unbiased sample matrix inversion (SMI) estimator as well as the (constraint) least-mean square, recursive least-squares, "multistage nested Wiener filter", and diagonally-loaded SMI filter estimators. Selecting the most successful (in some appropriate sense) AV filter estimator in the sequence for a given data record is a critical problem that has not been addressed so far. We deal exactly with this problem and we propose two data-driven selection criteria. The first criterion minimizes the cross-validated sample average variance of the AV filter output and can be applied to general filter estimation problems; the second criterion maximizes the estimated J-divergence of the AV filter output conditional distributions and is tailored to binary phase-shift-keying-type detection problems.

We investigate the relative output signal-to-interference-plus-noise ratio (SINR) performance of two linear direct-sequence code-division multiple-access multiuser detectors: the full decorrelator and the partial decorrelator. We derive necessary and sufficient conditions on the system parameters under which the partial decorrelator outperforms the full decorrelator in the output SINR sense. As a side study, we consider a blind implementation of the full decorrelator that is based on eigendecomposition of the interference-plus-noise autocovariance matrix and can be easily modified to provide a partial decorrelator. Simulation studies illustrate the relative SINR and bit-error rate performance of the full and partial decorrelator under perfectly known and sample-average-estimated input statistics.

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

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.

The local identical index (LII) associative memory (AM) proposed by the authors in a previous paper is a one-shot feedforward structure designed to exhibit no spurious attractors. In this paper we relax the latter design constraint in exchange for enlarged basins of attraction and we develop a family of modified LII AM networks that exhibit improved performance, particularly in memorizing highly correlated patterns. The new algorithm meets the requirement of no spurious attractors only in a local sense. Finally, we show that the modified LII family of networks can accommodate composite patterns of any size by storing (memorizing) only the basic (prime) prototype patterns. The latter property translates to low learning complexity and a simple network structure with significant memory savings. Simulation studies and comparisons illustrate and support the the optical developments.

We investigate the data-record-size requirements of the minimum-variance-distortionless-response beamformer to meet a given performance objective in signal detection and direction-of-arrival estimation problems. For signal detection problems we consider the output-energy performance measure while for direction-of-arrival estimation problems we adopt a spectrum-based measure defined as the ratio between the estimated and the ideal spectrum. In both cases, closed form expressions are derived that provide the data record size that is necessary to achieve a given performance confidence level in a neighborhood of the optimal performance point. This is done by utilizing close approximations of the involved probability density functions and Markoff-type inequalities. The practical significance of the derived expressions lies in the fact that the expressions are functions of the number of antenna elements only, while they are independent of the ideal input covariance matrix which is not known in most realistic applications.

We investigate the coarse synchronization performance of matched-filter-type (MF) and minimum-variance-distortion less-response-type (MF) near self-synchronized receivers for asynchronous direct-sequence code-division-multiple-access communications under finite data record adaptation. Analytic expressions are derived that approximate closely the probability of coarse synchronization error and provide low-cost highly-accurate alternatives to the computationally demanding performance evaluation through simulations. The expressions are explicit functions of the data record size N and the filter order p and reveal the effect of short-data-record sample-matrix-inversion (SMI) implementations on the coarse synchronization performance.

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

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.

Associative memories (AMs) can be implemented using networks with or without feedback. In this paper we utilize a two-layer feedforward neural network and propose a new learning algorithm that efficiently implements the association rule of a bipolar AM. The hidden layer of the network employs p neurons where p is the number of prototype patterns. In the first layer, the input pattern activates at most one hidden layer neuron or "winner." In the second layer, the "winner" associates the input pattern to the corresponding prototype pattern. The underlying association principle is minimum Hamming distance and the proposed scheme can be viewed also as an approximately minimum Hamming distance decoder. Theoretical analysis supported by simulations indicates that, in comparison with other suboptimum minimum Hamming distance association schemes, the proposed structure exhibits the following favorable characteristics: 1) It operates in one-shot which implies no convergence-time requirements; 2) it does not require any feedback; and 3) our case studies show that it exhibits superior performance than the popular linear system in a saturated mode (LSSM). The network also exhibits 4) exponential capacity and 5) easy performance assessment (no asymptotic analysis is necessary). Finally, since it does not require any hidden layer interconnections or tree-search operations, it exhibits low structural as well as operational complexity.

We consider the problem of robust detection of a spread-spectrum (SS) signal in the presence of unknown correlated SS interference and additive non-Gaussian noise. The proposed general SS receiver structure is comprised by a vector of adaptive chip-based nonlinearities followed by an adaptive linear tap-weight filter and combines the relative merits of both nonlinear and linear signal processing. The novel characteristics of our approach are as follows. First, the nonlinear receiver front-end adapts itself to the unknown prevailing noise environment providing robust performance for a wide range of underlying noise distributions. Second, the adaptive linear tap-weight filter that follows the nonlinearly processed chip samples results in a receiver that is proven to be effective in combating SS interference as well. To determine the receiver parameters, we propose, develop, and study three adaptive schemes under a joint mean-square error (MSE), or a joint bit-error-rate (BER), or a joint MSE-BER optimization criterion. As a side result, we derive the optimum decision fusion filter for receivers that utilize hard-limiting (sign) chip nonlinearities. Numerical and simulation results demonstrate the performance of the proposed schemes and offer comparisons with the conventional matched-filter (MF), the decorrelator, the conventional minimum-variance-distortionless-response (MVDR) filter, and the sign-majority vote receiver.

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).

A generalization of the Cramer-Rao (C-R) bound is derived for the p-th moment of any unbiased location parameter estimator. The relationship between the generalized C-R bound and the generalized Gaussian density is examined. A looser bound is also provided for the case of a mixture of generalized Gaussian densities as well as the case of multiple independent generalized Gaussian observations.>

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>

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