A novel Integrated Sensing-Communication (ISAC) system is proposed that can accommodate high mobility scenarios while making efficient use of bandwidth for both communication and sensing. The system comprises a monostatic multiple-input multiple-output (MIMO) radar that transmits orthogonal time frequency space (OTFS) waveforms. Bandwidth efficiency is achieved by making Doppler-delay (DD) domain bins available for shared use by the transmit antennas. For maximum communication rate, all DD-domain bins are used as shared, but in this case, the target resolution is limited by the aperture of the receive array. A low-complexity method is proposed for obtaining coarse estimates of the radar targets parameters in that case. A novel approach is also proposed to construct a virtual array (VA) for achieving a target resolution higher than that allowed by the receive array. The VA is formed by enforcing zeros on certain time-frequency (TF) domain bins, thereby creating private bins assigned to specific transmit antennas. The TF signals received on these private bins are orthogonal, enabling the synthesis of a VA. When combined with coarse target estimates, this approach provides high-accuracy target estimation. To preserve DD-domain information, the introduction of private bins requires reducing the number of DD-domain symbols, resulting in a trade-off between communication rate and sensing performance. However, even a small number of private bins is sufficient to achieve significant sensing gains with minimal communication rate loss.

With the emerging privacy sensitive applications, anonymity is recognized as an important attribute in privacy-preserving communications. Existing anonymous precoding approaches at the physical (PHY) layer aim to mask sender’s channel characteristic while overlooking the fact that other PHY characteristics contain information that can be traced back to the sender. In this paper, we first reveal that the sender’s in-phase and quadrature-phase imbalance (IQI) characteristic of its radio frequency (RF) front end can also be exploited as unique signature of the sender. Since the signal is transmitted through the RF front end and then is propagated through the channel, the received signal carries the composite characteristics of the IQI and channel which can be exploited to identify the sender. To prevent the sender detection enhanced by IQI, an IQI aware anonymous alias sender construction scheme is proposed with the concept of Kullback-Leibler divergence (KLD). It manipulates the signaling of transmitted signal, so that from the perspective of detector, the detection statistic of the real sender is close to that of the alias. To mitigate the communication performance loss caused by the IQI, we exploit the IQI interference as a constructive element. While the constructive IQI design increases the degree of freedom of precoding design, it does not violate the sender anonymity requirement. Finally, an IQI aware anonymous precoder (IAA) is proposed. Simulation demonstrates that the anonymity and communication performance of the proposed IAA precoder is maintained at a high level and is robust to the IQI parameters, where existing approaches fail.

The paper considers monostatic ISAC transceivers relying on in-band full-duplex (IBFD) capability to achieve simultaneous sensing and communication. These systems transmit a waveform for both communication and sensing and receive target echoes and incoming communication signals from other nodes. The major challenge is interference cancellation, suppressing self-interference (SI) from the leaked transmitted signal and the mutual interference (MI) between the echo for sensing and incoming signals for communication. This paper proposes an advanced joint analog and two-stage digital interference cancellation (DIC) structure to address this challenge, enabling simultaneous communication and sensing in IBFD ISAC systems. The analog SI cancellation structure leverages an analog least mean square (ALMS) loop with specific design constraints to preserve the integrity of sensing signals. A track-and-hold mechanism is employed to avoid ALMS weighting coefficient variation caused by the strong reflected sensing signal and uplink communication signal. The novel two-stage DIC first cancels residual SI for sensing and then mitigates echo sensing signals for communication. Doppler effects in the echo signals are compensated during the second stage to ensure effective suppression of sensing signals and accurate retrieval of communication signals. Simulation results validate the proposed approach, showcasing its strong communication and sensing performance and robust interference cancellation capabilities.

A novel dual-function radar communication (DFRC) system is proposed that can accommodate high mobility scenarios while making efficient use of bandwidth for both communication and sensing. The system comprises a monostatic multiple-input multiple-output (MIMO) radar that transmits orthogonal time frequency space (OTFS) waveforms. Bandwidth efficiency is achieved by making all Doppler-delay (DD) domain bins available for all transmit antennas. While this shared use of DD bins allows for high communication rate, it makes target estimation challenging. A low complexity method is proposed to estimate the radar parameters at the receiver. The proposed system not only exhibits the advantages of other multi-carrier waveforms, but is also more robust to Doppler frequency shifts that arise in high mobility scenarios. Simulation results demonstrate the promise of the proposed approach in 6G system.

Monitoring vital signs such as breathing rate (BR) and heart rate (HR) is crucial for early detection of health issues and supports a wide range of health-related applications. Traditional monitoring methods often involve body-attached medical devices, which can be intrusive and inconvenient for continuous use in daily life. Contactless monitoring using radio frequency (RF) signals has emerged as a promising alternative, but acquiring precise vital sign measurements remains challenging due to the limited sensing resolution of RF devices. In this paper, we design a high-resolution contactless vital sensing system by leveraging advanced beamforming in combination with machine learning (ML) methods. The key idea of our system is to reconstruct fine-grained vital sign measurements from RF signals, achieving low estimation error, comparable to that of dedicated medical devices such as photoplethysmography sensors, respiration monitoring belts. To enhance the reconstruction performance, we integrate an antenna array with double phase shifters to acquire RF data that captures precise chest displacement of human subjects. An encoder-decoder model based on a 1D convolutional neural network is then developed to map the RF signals into vital sign measurements. Extensive evaluations show that our system has low errors of 0.3 beat per minute (BPM) for BR estimation and 2.7 BPM for HR estimation.

Vital sign monitoring via a phased array radar transmitting continuous-wave (CW) or linear frequency-modulated continuous-wave (FMCW) offers contactless, low-cost, continuous monitoring of vital signs, i.e., human heart rate and breathing rate. In this paper, we demonstrate that, besides the authorized receiver of radar echoes, a passive eavesdropper could process the reflections of the CW or linear FMCW waveforms off the patient being monitored and decipher their vital signals, thus violating their privacy. We then propose an approach to protect vital sign privacy. Specifically, we propose the use of a nonlinear FMCW waveform that distorts the radar echoes, making it more difficult for the eavesdropper to extract the vital signs without knowledge of the specific parameters of the non-linear FMCW waveform. At the same time, the authorized receiver, with knowledge of those parameters, can easily extract the vital signs. Therefore, the proposed waveform can simultaneously implement vital sign monitoring and user privacy protection. Numerical experiments demonstrate the effectiveness of our proposed scheme.

Uncrewed vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the sixth generation (6G) of wireless networks. Their seamless integration, cost-effectiveness, and additional controllability through motion planning make them an attractive deployment option for a wide range of applications, both as assets in the network e.g., mobile base stations (BSs) and as consumers of network services (e.g., autonomous delivery systems). However, despite their potential, the convergence of UVs and wireless systems brings forth numerous challenges that require attention from both academia and industry. This article then aims to offer a comprehensive overview, encompassing the transformative possibilities as well as the significant challenges associated with UV-assisted next-generation wireless communications. Considering the diverse landscape of possible application scenarios, problem formulations, and mathematical tools related to UV-assisted wireless systems, the underlying core theme of this article is the unification of the problem space, providing a structured framework to understand the use cases, problem formulations, and necessary mathematical tools. Overall, this article sets forth a clear understanding of how UVs can be integrated in the 6G ecosystem, paving the way toward harnessing the full potential at this intersection.

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the derived Cramér-Rao lower bound (CRLB) expression of the localization accuracy unveils a significant finding: DeployingNISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the$\ln ^{2}N$scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of$N^{2}$achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance. Simulation results demonstrate that compared to the conventional time-sharing scheme or a non-cooperative scheme, the proposed cooperative ISAC scheme can effectively improve the average data rate and reduce the CRLB, hence striking an improved S&C performance tradeoff at the network level.

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design methods through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform’s ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.

Time-modulated arrays (TMAs) transmitting information bearing orthogonal frequency division multiplexing (OFDM) signals can achieve directional modulation. By turning its antennas on and off in a periodic fashion, the TMA can be configured to transmit the OFDM signal undistorted in the direction of a legitimate receiver and scrambled everywhere else. This capability has been proposed as means of securing the transmitted information from unauthorized users. In this paper, we investigate how secure the TMA OFDM system is, by looking at the transmitted signal from an eavesdropper’s point of view. We demonstrate that the symbols observed by the eavesdropper across the OFDM subcarriers are linear combinations of the source symbols, with mixing coefficients that are unknown to the eavesdropper. We propose the use of independent component analysis (ICA) theory to obtain the mixing matrix and provide methods to resolve the column permutation and scaling ambiguities, which are inherent in the ICA problem, by leveraging the structure of the mixing matrix and assuming knowledge of the characteristics of the TMA OFDM system. In general, resolving the ambiguities and recovering the symbols requires long data. Specifically for the case of the constant modulus symbols, we propose a modified ICA approach, namely the constant-modulus ICA (CMICA), that provides a good estimate of the mixing matrix using a small number of received samples. We also propose countermeasures which the TMA could undertake in order to defend the scrambling. Simulation results are presented to demonstrate the effectiveness, efficiency and robustness of our scrambling defying and defending schemes.

Deep Q-learning stands as an integral component within modern deep reinforcement learning algorithms. Notwithstanding its recent successes, deep Q-learning can be susceptible to instability and divergence, especially when combined with off-policy learning and bootstrapping, a combination also referred to as the "deadly triad". The current work introduces a novel learning process that aligns with the flow of gradient-based meta-learning algorithms and is designed to be performed prior to the application of deep Q-learning. The primary goal of the proposed learning process is to instill favorable generalization properties within the Q-function approximator, by conditioning its corresponding Neural Tangent Kernel. The proposed approach is applied on a sample of the environments of the DeepMind Control Suite and provides about 15% improvement in average reward accumulation.

Physical layer security is a common concern in dual-function radar communications (DFRC) because of sharing of information between different emitters. We study covert communications between a DFRC unit and multiple legitimate users, with assistance from an intelligent reflecting surface (IRS). The system has multiple targets that need to be detected, and each target is collocated with a warden trying to detect the ongoing communication. We seek to maximize the worst-case data rate across users under radar detection constraint and covertness constraint. To this end, we superpose artificial noise with our message signal so that the wardens’ received signal statistics do not change significantly if communications suddenly starts. We formulate a highly non-convex optimization problem to determine the passive beamforming scheme for the IRS and active precoding scheme at the transmitter, and solve it using a combination of auxiliary matrices, alternating optimization, and a variant of stochastic gradient descent. Finally, we validate the proposed algorithm numerically.

This paper tackles the challenge of wideband MIMO channel estimation within indoor millimeter-wave scenarios. Our proposed approach exploits the integrated sensing and communication paradigm, where sensing information aids in channel estimation. The key innovation consists of employing both spatial and temporal sensing modes to significantly reduce the number of required training pilots. Moreover, our algorithm addresses and corrects potential mismatches between sensing and communication modes, which can arise from differing sensing and communication propagation paths. Extensive simulations demonstrate that the proposed method requires 4× less pilots compared to the current state-of-the-art, marking a substantial advancement in channel estimation efficiency.

Time-modulated arrays (TMA) transmitting orthogonal frequency division multiplexing (OFDM) waveforms achieve physical layer security by allowing the signal to reach the legitimate destination undistorted, while making the signal appear scrambled in all other directions. In this paper, we examine how secure the TMA OFDM system is, and show that it is actually possible for the eavesdropper to defy the scrambling. In particular, we show that, based on the scrambled signal, the eavesdropper can formulate a blind source separation problem and recover data symbols and TMA parameters via independent component analysis (ICA) techniques. We show how the scaling and permutation ambiguities arising in ICA can be resolved by exploiting the Toeplitz structure of the corresponding mixing matrix, and knowledge of data constellation, OFDM specifics, and the rules for choosing TMA parameters. We also introduce a novel TMA implementation to defend the scrambling against the eavesdropper.

We consider an OFDM transmitter aided by an intelligent reflecting surface (IRS) and propose a novel approach to enhance waveform security by employing time modulation (TM) on the IRS side. By controlling the periodic TM pattern of the IRS elements, the system is designed to preserve communication information towards an authorized recipient and scramble the information in all other directions. Compared with applying TM at the transmitter, the substantial beam-forming gain from the IRS compensates for the power loss due to the deactivation of radiating elements during the use of TM, and also the scrambling is realized in both azimuth and elevation directions. We introduce two modes of TM pattern control, namely, the linear and planar modes. While the linear mode is simpler to implement compared to the planar mode, it does introduce sidelobes, over which the transmitted information is not adequately scrambled. We demonstrate how the sidelobes of the linear mode can be effectively suppressed by leveraging the high diversity inherent in that mode.

Departing from traditional data security-oriented designs, the aim of anonymity is to conceal the transmitters’ identities during communications to all possible receivers. In this work, joint anonymous transceiver design at the physical (PHY) layer is investigated. We first present sender detection error rate (DER) performance analysis, where closed-form expression of DER is derived for a generic precoding scheme applied at the transmitter side. Based on the tight DER expression, a fully DER-tunable anonymous transceiver design is demonstrated. An alias channel-based combiner is first proposed, which helps the receiver find a Euclidean space that is close to the propagation channel of the received signal for high quality reception, but does not rely on the recognition of the real sender’s channel. Then, two novel anonymous precoders are proposed under a given DER requirement, one being able to provide full multiplexing performance, and the other flexibly adjusting the number of multiplexing streams with further consideration of the receive-reliability. Simulation demonstrates that the proposed joint transceiver design can always guarantee the subscribed DER performance, while well striking the trade-off among the multiplexing, diversity and anonymity performance.

Speech eavesdropping has long been an important threat to the privacy of individuals and enterprises. Recent research has shown the possibility of deriving private speech information from sound-induced vibrations. Acoustic signals transmitted through a solid medium or air may induce vibrations upon solid surfaces, which can be picked up by various sensors (e.g., motion sensors, high-speed cameras and lasers), without using a microphone. To date, these threats are limited to scenarios where the sensor is in contact with the vibration surface or at least in the visual line-of-sight.

Recognizing the low-cost advantage of phased arrays, we investigate the improvement of the beamforming capability of a phased array via the use of double-phase shifters (DPS), i.e., each antenna is fed with the sum of the outputs of two phase shifters. The use of DPS allows for the manipulation of both the magnitude and phase of the signal transmitted by each antenna, thus enabling the formation of a flexible beam toward the desired target while suppressing the energy radiated toward unwanted directions. By sequentially steering the mainbeam to each target, while nulling other targets, one can monitor the vital signals of multiple targets with low inter-target interference. This is achieved at only a modest increase in the cost of a phased array due to the addition of phase shifters. The performance of the proposed approach is demonstrated via a prototype DPS phased array with 4 Vivaldi antennas using 8 commercial phase shifters and transmitting a continuous-wave RF signal at center frequency of 2.2 GHz.

We consider an integrated sensing-communication system operating in a dynamic environment, such as an autonomous vehicle scenario. We propose a novel, low-cost, low power consumption and low-computation approach for designing a beam that can simultaneously reach the radar target of interest and the desired communication destination. The transmitter is a uniform linear array, equipped with quantized double phase shifters, which enables a flexible beam design while using analog only processing. Only a small number of antennas are selected to transmit in each channel use, in order to save system power and reduce antenna coupling. We propose a deep reinforcement learning approach to adaptively adjust the double phase shifters and select the active antennas in order to optimize the transmit beamforming, through a transmission and feedback trail. The actor-critic network strategy together with the Wolpertinger policy is adopted to obtain the optimal solutions efficiently and effectively. Numerical results demonstrate the feasibility of the proposed method.

Speech privacy leakage has long been a public concern. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we investigate a new form of attack that remotely elicits speech from minute surface vibrations upon common room objects (e.g., paper bags, plastic storage bin) via mmWave sensing. We design and implement a highresolution software-defined phased-MIMO radar that integrates transmit beamforming, virtual array, and receive beamforming. The proposed system enhances sensing directivity by focusing all the mmWave beams toward a target room object. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple digits.

The paper studies the motion control for mobile relays implementing cooperative beamforming to aid the communication between a source-destination pair. We consider an urban communication scenario, where the channels exhibit spatiotemporal correlations and thus can be learned. The relays move in a time-slotted fashion within a three-dimensional cube. During every slot, the relays beamform optimally to maximize the Signal-to-Interference+Noise Ratio (SINR) at the destination and decide their positions for the next slot. Unlike prior works that assume knowledge of channel statistics, our proposed approach is model-free. Also, typically, prior approaches assume discrete motion on the two-dimensional plane. However, as discretization introduces the curse of dimensionality, those methods do not easily extend to three-dimensional motion. We propose a model-free, continuous control actor-critic approach that can be easily applied to 2D and 3D motion with the same complexity. To address the random nature of the channel, we propose to use Sinusoidal Representation Networks (SIRENs) for value function approximation. Our approach outperforms the direct application of the State-of-the-Art continuous control algorithms for both 2D and 3D cases.

With the use of common signaling methods for dual-function radar-communications (DFRC) systems, the susceptibility of eavesdropping on messages aimed at legitimate users has worsened. For DFRC systems, the radar target may act as an eavesdropper (ED) that receives a high-energy signal thereby leading to additional challenges. Unlike prior works, we consider a multicast multi-antenna DFRC system with multiple EDs. We then propose a physical layer design approach to maximize the secrecy rate by installing intelligent reflecting surfaces in the radar channels. Our optimization of multiple ED multicast multi-antenna DFRC secrecy rate (OptM3Sec) approach solves this highly nonconvex problem with respect to the precoding matrices. Our numerical experiments demonstrate the feasibility of our algorithm in maximizing the secrecy rate in this DFRC setup.

We consider multi-input multi-output (MIMO) dual function radar communication (DFRC) systems, and design a transmit beamforming matrix that optimizes a weighted combination of the radar estimate Cramer-Rao bound (CRB) and the communication rate. A hybrid beamforming structure is considered, with fewer RF chains than antennas, to achieve the benefits of MIMO systems while maintaining low cost. However, such a structure may have a rank-deficient beamforming matrix, resulting in degraded estimation performance. We propose antenna selection as means to ensure a full-rank beamforming matrix, and also select the communication channels so that high communication rate can be achieved. A learning approach is employed to optimally select antennas and design the corresponding beamforming matrix. By leveraging a combination of softmax neural networks, the proposed solution is able to optimize the joint performance metric for a DFRC system.

Speech privacy leakage has long been a public concern. Through speech eavesdropping, an adversary may steal a user's private information or an enterprise's financial/intellectual properties, leading to catastrophic consequences. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we discover a new form of speech eavesdropping attack that senses minor speech-induced vibrations upon common room objects using mmWave. By integrating phasedarray and multiple-input and multiple-output (MIMO) on a single mmWave transceiver, our attack can capture and fuse micrometerlevel vibrations upon the surfaces of multiple objects to reveal speech content in a remote and non-line-of-sight fashion. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple speech content.

Dual function radar communication (DFRC) systems can achieve significant improvements in spectrum efficiency, system complexity and energy efficiency, and are attracting a lot of attention for next generation wireless system design. This paper considers DFRC systems using MIMO radar with a sparse transmit array, transmitting OFDM waveforms, and assigning shared and private subcarriers to active transmit antennas. Sub-carrier sharing allows antennas to modulate data symbols onto the same subcarriers and enables high communication rate, while the use of private subcarriers trades-off communication rate for sensing performance by enabling the formulation of a virtual array with larger aperture than the physical receive array. We propose to exploit the permutation of private subcarriers among the available subcarriers and the pairing between active antennas and private subcarriers to recover some of the communication rate loss. Exploiting the 1-sparse property of private subcarriers, we also propose a low complexity algorithm to identify private subcarriers and detect the antenna-subcarrier pairing.

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal.

The paper considers a multi-access channel scenario, where several users communicate with a base station, and investigates power allocation is a game-theoretic framework. The communication metric is the inverse signal-to-interference-plus-noise ratio (SINR) at the base station, which, for low SINR reflects communication delay. Each user faces a trade-off between the latency of the signal received by the base station, and the price that the user pays for using a specific amount of power that causes interference in the system. The equilibrium is derived in closed form and its uniqueness is proven. It is shown that the resulting strategy allows each user to maintain uninterrupted communication. For comparison purposes, we construct a specific three user network scenario, and study the SINR and throughput metrics. In that setting, we show that, unlike the latency metric, SINR and throughput may give rise to multiple equilibria, which may cause destabilization of communication.

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy while incurring low cost.

Wireless Autonomous Networks are expected to support communication between a source and a receiver, by constantly self-adapting to changes in their communication environment. This paper considers a scenario of relay beamforming, in which relays collaboratively retransmit the source signal so that they maximize the average signal-to-interference+noise ratio (SINR) at the destination. The relays are grouped into clusters, with each cluster having a single active relay at a time. The system evolves in time slots; in each time slot the clusters beamform to the destination, and at the same time, each cluster selects the relay to be active in the subsequent time slot. Relay selection is performed locally within each cluster, using a reinforcement learning approach, namely Q-learning. Compared to prior methods, the proposed scheme does not require any statistical knowledge on the channels, and achieves similar average SINR performance while involving lower complexity.

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. The user switches between two different modes, i.e., the (a) regular transmission mode, according to which both players follow a Nash equilibrium; and the (b) smart transmission mode, according to which the user always implements the best response strategy. First, we consider the case in which the switching between transmission modes occurs with a given frequency. For this case we find the optimal transmission power of the user by formulating and solving a Bayesian game problem. We show that an increase in the frequency of smart transmissions leads to a decrease in communication latency and to an increase in the total transmission cost. We determine the switching frequency that optimizes the latency-cost trade off using α-fairness criteria. We also discuss the implications of the proposed latency metric on the player strategies as compared to the previously well studied signal-to-interference-plus-noise ratio (SINR) metric.

We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA beampattern is low. Prior approaches have focused on optimal sparse array design, or use of interpolation techniques for filling the holes in the synthesized SLA before applying digital beamforming for angle finding. In this paper, different from previous efforts, we use matrix completion to complete the corresponding virtual uniform linear array (ULA) before estimating the target angle. In particular, we show that for a small number of targets within the same range-Doppler cell, the Hankel matrix constructed by subarrays of the virtual ULA is low-rank, and thus under certain conditions, can be completed based on the SLA measurements. We derive the coherence properties of the Hankel matrix so that the matrix can be competed via nuclear norm minimization methods. We also demonstrate via examples the effect of various SLA topologies on the identifiability of the Hankel matrix.

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework. A player (the user or the jammer) has incomplete information about its rival's identity in the form of uncertainty; the player only knows the probabilities that its rival is a player implementing a behavioral strategy as a follower in a Stackelberg game (smart-type), or selects a feasible strategy as in a Nash game (regular-type). We model the problem as two Bayesian games. In the first game, the user has incomplete information about the jammer, and in the second game, the jammer has incomplete information about the user. The user's utility is throughput. We prove that a unique equilibrium exists and derive it in closed form as a function of the known probabilities. We show that the Nash and Stackelberg equilibria are boundary cases of the obtained equilibrium. Thus, our approach allows one to incorporate the Nash and Stackelberg equilibria into a unified scale of equilibria. Monotonicity properties of the equilibrium strategies and the corresponding payoffs with respect to the network parameters are proven, and also supported by simulations.

To satisfy the increasing demand for additional bandwidth from the wireless sector, regulatory bodies are considering to allow commercial wireless systems to operate on spectrum bands that until recently were reserved exclusively for military radar. Such co-existence would require mechanisms for controlling interference. One such mechanism is to assign a precoder to the communication system, which is designed to minimize the communication system’s interference to the radar. This paper looks into whether the implicit radar information contained in such a precoder can be exploited by an adversary to infer the radar’s location. For two specific precoder schemes, we simulate a machine learning based location inference attack. We show that the system information leaked through the precoder can indeed pose various degrees of risk to the radar’s privacy, and further confirm this by computing the mutual information between the respective precoder and the radar location.

Due to the shared and open-access nature of the wireless medium, wireless networks are vulnerable to jamming attacks. In this paper we study the problem of resource assigning in a single carrier communication system, where a user is communicating with a destination in the presence of a jammer. The jammer's channel to the destination is assumed flat fading, and its gain is known in probabilistic terms. In particular, the jammer's channel gain could take any value out of a finite set, with an a priori known probability. We model the problem in a Bayesian jamming game framework with utility the user throughput. We prove the existence and uniqueness of Nash and Stackelberg equilibria, and derive the equilibrium strategies in closed form. Our theoretical results, also supported by simulations, suggest that the Nash strategy is more sensitive to varying a priori probabilities, as compared to the Stackelberg strategy.

Block Sparse Bayesian Learning (BSBL) methods estimate a block sparse vector by maximizing the posterior distribution and using sparsity-inducing priors. In BSBL works, all hyperparameters priors are assumed to follow the same distribution with the same parameters. In this paper, we propose to assign different parameters to each hyperparameter, giving more importance to some hyperparameters over others. The importance weights are obtained by leveraging a low resolution estimate of the underlying sparse vector, for example, an estimate obtained via a method that does not encourage sparsity. We refer to the proposed approach as Weighted Block Sparse Bayesian Learning (WBSBL). Simulation results show that, as compared to BSBL, WBSBL achieves substantial improvement in terms of probability of detection and probability of false alarm in the low signal to noise ratio regime. Also, WBSBL's performance degrades slower than that of BSBL as the number of active blocks increases.

We propose a multidimensional sparse Fourier transform inspired by the idea of the Fourier projection-slice theorem, called FPS-SFT. FPS-SFT extracts samples along lines (1-dimensional slices from a multidimensional data cube), which are parameterized by random slopes and offsets. The discrete Fourier transform (DFT) along those lines represents projections of multidimensional DFT of the data onto those lines. The multidimensional frequencies that are contained in the signal can be reconstructed from the DFT along lines with a low sample and computational complexity provided that the signal is sparse in the frequency domain and the lines are appropriately designed. The performance of FPS-SFT is demonstrated both theoretically and numerically. A sparse image reconstruction application is illustrated, which shows the capability of the FPS-SFT in solving less sparse scenarios containing non-uniformly distributed frequencies.

As women faculty in electrical and computer engineering (ECE), we have been involved in several efforts targeted at increasing the participation of women in ECE departments. For example, at Rutgers University, we have organized presentations and workshops for first year students that highlighted the societal aspects of ECE, such as bioelectrical engineering applications. Such focus did bear fruit; between 2010 and 2016, the sophomore female enrollment in ECE at Rutgers rose from 11% to 19%. At the University of San Diego, we have conducted research on the demographics and outcomes of undergraduate students in ECE, facilitated workshops to help faculty teach in more inclusive ways, and are currently working on a National Science Foundation (NSF)-funded effort to revolutionize engineering education [1]. Also, as chairs, we have been considering ways to diversify our faculty carefully considering the overall hiring process including innovative approaches such as cluster hires [2], paying close attention to the language used when advertising faculty positions, the discussions during the review of the candidates, the candidate selection criteria, and the interview process [3]. However, the number of diverse graduate students is small to begin with, and in an era in which companies have realized the value of diversity, academia has to compete with companies such as Google and Microsoft for the best Ph.D. graduates. Without a diverse faculty, we cannot sustain a diverse student body.

This paper investigates and quantifies the advantages of a Full-Duplex (FD) transmitter/receiver pair in improving the secrecy rate of the system. We consider a linear precoder design for a multiple-input multiple-output Gaussian wiretap channel, which comprises two legitimate nodes, i.e., Alice and Bob, operating in FD mode and exchanging confidential messages in the presence of a passive eavesdropper. Using the sum secrecy degrees of freedoms (sum SDoFs) as metric, we formulate an optimization problem with respect to Alice's and Bob's precoding matrices. In order to solve this problem, we first propose a cooperative secrecy transmission scheme, whose feasible set is sufficient to achieve the maximum sum SDoF. Based on that feasible set, we then determine in closed form the maximum achievable sum SDoF and also provide a method for constructing the precoding matrix pair, which achieves the maximum sum SDoF. The latter pair would be near-optimal in terms of the achievable secrecy sum rate in the high signal-to-noise ratio (SNR) regime. By providing the maximum achievable sum SDoF as a function of the number of antennas, one could select the optimal system parameters to further maximize the achievable sum SDoF. We use simulations to evaluate the performance of the proposed precoding matrices in realistic channel scenarios and at various levels of the SNR.

Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors.

We address the problem of enhancing Quality-of-Service (QoS) in power constrained, mobile relay beamforming networks, by controlling the motion of the relaying nodes. We consider a time slotted system, where the relays update their positions before the beginning of each time slot. Adopting a spatiotemporal stochastic field model of the wireless channel, we propose a novel 2-stage stochastic programming formulation for specifying the relay positions at each time slot, such that the QoS of the network is maximized on average, based on causal Channel State Information (CSI) and under a total relay transmit power budget. Via the Method of Statistical Differentials, the motion control problem considered is shown to be approximately equivalent to a set of simple subproblems, which are solved in a distributed fashion, one at each relay. Numerical simulations are also presented, corroborating the efficacy of the proposed approach.

In this paper, we propose a new matrix completion based MIMO radar (MIMO-MC) using a random unitary matrix as the waveform matrix. We show that the corresponding data matrix has a good incoherent property, which guarantees accurate reconstruction of the data matrix from partial entries. The derived performance guarantees hold for any random unitary waveforms and radar precoder. This indicates that the proposed MIMO-MC approach can dynamically adapt its waveform/precoding for the purpose of waveform security or interference suppression in low SINR conditions, without affecting the performance of data matrix completion. We further investigate the application of MIMO-MC transmit precoding for clutter mitigation and spectrum sharing with wireless communications.

As compared to the FFT, the recently introduced Sparse Fourier Transform (SFT) achieves substantial reduction in the complexity of detecting frequencies in signals that are sparse in the frequency domain. However, the SFT requires the significant frequencies to be on the grid and the exact sparsity of the signal to be known. In this paper, we propose a framework that overcomes these issues. Our method makes use of a pre-permutation window to confine the leakage within finite frequency bins and the Neyman-Pearson criterion to detect weak signals without knowing the exact signal sparsity. Various numerical experiments and an application to radar target detection demonstrate the advantages of the proposed method.

The paper presents the average signal-to-clutter loss (SCRL) analysis for polarimetric space-time adaptive processing by exploiting the Kronecker structure of the clutter covariance matrix (CM). An expression for the average SCRL as a function of the mean square error of the corresponding CM estimator is derived. Based on that expression, one can determine how many samples are required in order to achieve a desired SCRL. The proposed average SCRL analysis methodology can be extended to more general scenarios, where closedform CM estimates are not available. Simulations indicate that even in the non-asymptotic regime, the proposed method can provide a good prediction of the average SCRL.

This paper considers a scenario in which an Alice-Bob pair wishes to communicate in secret in the presence of an active Eve, who is capable of jamming as well as eavesdropping and operates in full-duplex (FD) mode. As countermeasure, Bob operates in FD mode, using a subset of its antennas to receive, and the remaining antennas to transmit jamming noise. Alice and Bob employ linear precoding, and all nodes use Gaussian code books. In that context, our goal is to maximize the achievable secrecy degrees of freedom (S.D.o.F.) of the system. We provide the optimal receive/transmit antennas allocation at Bob, based on which we determine in closed form the maximum achievable S.D.o.F. We also provide a method for constructing the precoding matrices of Alice and Bob, based on which the maximum S.D.o.F. can be achieved. We further investigate the adverse scenario in which Eve knows Bob's transmission strategy and optimizes its transmit/receive antennas allocation in order to minimize the achievable S.D.o.F. For that case, we find the worst case achievable S.D.o.F. Numerical results validate the theoretical findings and demonstrate the performance of the proposed method.

We consider stochastic motion planning in single-source singledestination robotic relay networks, under a cooperative beamforming framework. Assuming that the communication medium constitutes a spatiotemporal stochastic field, we propose a 2-stage stochastic programming formulation of the problem of specifying the positions of the relays, such that the expected reciprocal of their total beamforming power is maximized. Stochastic decision making is made on the basis of random causal CSI. Recognizing the intractability of the original problem, we propose a lower bound relaxation, resulting to a nontrivial optimization problem with respect to the relay locations, which is equivalent to a small set of simple, tractable subproblems. Our formulation results in spatial controllers with a predictive character; at each time slot, the new relay positions should be such that the expected power reciprocal at the next time slot is maximized. Quite remarkably, the optimal control policy to the relaxed problem is purely selective; under a certain sense, only the best relay should move.

This paper considers secrecy communication from a signal processing point of view, and studies the maximal achievable secrecy degrees of freedoms (S.D.o.F.) of a helper-assisted Gaussian wiretap channel, consisting of a source, a legitimate receiver, an eavesdropper and an external helper. Each terminal is equipped with multiple antennas. We first propose a cooperative secrecy transmission scheme, and show that it achieves the maximal secrecy degrees of freedom. We then propose a heuristic method, through which, we solve analytically the optimization problem associated with the proposed cooperative secrecy transmission scheme. By this way, we obtain the maximal achievable S.D.o.F. and also the precoding matrices which achieve the maximal S.D.o.F. in closed-form.

A joint design approach is proposed for the coexistence of MIMO radars and a communication system, for a scenario in which the targets fall in different range bins. Radar transmit precoding and adaptive communication transmission are adopted, and are jointly designed to maximize signal-to-interference-plus-noise ratio (SINR) at the radar receiver subject to the communication system meeting certain rate and power constraints. We start with the design of a system in which knowledge of the target information is used. Such design can be used to benchmark the performance of schemes that do not use target information. Then, we propose a design which does not require target information. In both cases, the optimization problems are nonconvex with respect to the design variables and have high computational complexity. Alternating optimization and sequential convex programming techniques are used to find a local maximum. Based on the analysis of the obtained solution, we propose a reduced dimensionality design, which has reduced complexity without degrading the radar SINR. Simulation results validate the effectiveness of the proposed spectrum sharing framework.

Recently proposed multiple input multiple output radars based on matrix completion (MIMO-MC) employ sparse sampling to reduce the amount of data forwarded to the radar fusion center, and as such enable savings in communication power and bandwidth. This paper proposes designs that optimize the sharing of spectrum between MIMO-MC radars and MIMO communication systems, so that the latter interferes minimally with the former. First, the communication system transmit covariance matrix is designed to minimize the effective interference power (EIP) at the radar receiver, while maintaining certain average capacity and transmit power for the communication system. Two approaches are proposed, namely a noncooperative and a cooperative approach, with the latter being applicable when the radar sampling scheme is known at the communication system. Second, a joint design of the communication transmit covariance matrix and the MIMO-MC radar sampling scheme is proposed, which achieves even further EIP reduction.

The paper proposes a matrix completion based colocated MIMO radar (MIMO-MC) approach that employs transmit beamforming. The transmit antennas transmit correlated waveforms to illuminate certain directions. Each receive antenna performs sub-Nyquist sampling of the target returns at uniformly random times, and forwards the samples to a fusion center along with information on the sampling times. Based on the forwarded samples, the fusion center partially fills a matrix, recovers the Nyquist rate samples via matrix completion, and subsequently proceeds with target estimation via standard techniques. The performance of matrix completion depends on the matrix coherence. The paper derives the relations between transmit waveforms and matrix coherence. Specifically, it is shown that, for a rank-1 beamformer, the coherence is optimal, i.e., 1, if and only if the waveforms are unimodular. For a multi-rank beamformer, the coherence of the row space of the data matrix is optimal if the waveform power is constant across each snapshot. Simulation results show that the proposed scheme achieves high resolution with a significantly reduced number of samples.

We consider a cooperative secret communication scenario, in which a group of mobile and power constrained nodes, acting as relays, cooperatively transmit to a destination in the presence of an eavesdropper; both destination and eavesdropper are assumed stationary. The cooperative scheme entails motion control and optimal communication, in order to achieve a prescribed level of ergodic secrecy rate. The group of motion-controlled cooperating relays is here termed as mobi-clique. Under this setting, a novel, decentralized motion control scheme is derived, which effectively drives the relays to a formation configuration, so that a prescribed expected secrecy requirement is met, while at the same time the utilization of network resources is maximized. The effectiveness of the proposed approach is verified both theoretically and through numerical simulations.

This paper considers the problem of multiple distributed nodes in a wireless network wishing to join forces and cooperatively beamform data to a destination. Since the nodes are not physically connected and have independent oscillators, there are carrier frequency and phase errors that degrade the beamforming performance. This paper proposes a novel open-loop synchronization protocol, which exploits the broadcast nature of the wireless channel to achieve synchronization much faster than prior methods, with the difference increasing as the number of nodes increases.

We consider a source (Alice) trying to communicate with a destination (Bob), in a way that an unauthorized node (Eve) cannot infer, based on her observations, the information that is being transmitted. The communication is assisted by multiple multi-antenna cooperating nodes (helpers) who have the ability to move. While Alice transmits, the helpers transmit noise that is designed to affect the entire space except Bob. We consider the problem of selecting the helper weights and positions that maximize the system secrecy rate. It turns out that this optimization problem can be efficiently solved, leading to a novel decentralized helper motion control scheme. Simulations indicate that introducing helper mobility leads to considerable savings in terms of helper transmit power, as well as total number of helpers required for secrecy communications.

We consider cooperative transmission in wireless relay networks, in which a source communicates with the destination with the help of a set of N cooperating amplify-and-forward relays. The relay weights are obtained to maximize the received signal-to-noise ratio at the destination, subject to individual power constraint. We consider two schemes that have appeared in the literature, i.e., (i) the optimal weight vector design method, which has been solved via second-order cone programming plus a bisection search, with complexity of O(N3.5), and (ii) the one-bit feedback phase control scheme, which has been formulated as a binary quadratic programming and has been solved for exact solution via exhaustive search. We propose algorithms for these two problems that have substantially reduced complexity, i.e., O(N log2N) or O(N) for the first problem, and polynomial time O(N log2N) for the second problem.

We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.

A wireless network with one source, one destination, one eavesdropper, and multiple decode-and-forward relays is considered. A two-slot cooperative relaying scheme is proposed that targets at maximizing the secrecy rate. In the first slot, the source transmits the information bearing signal, and at the same time, it cooperates with the destination in jamming the eavesdropper without creating interference at the relay. In the second slot, one optimally selected relay retransmits the decoded source signal, and at the same time, that particular relay cooperates with the source to jam the eavesdropper without creating interference at the destination. Optimal relay selection and also optimal power allocation among the first/second slot data signal and jamming noise are proposed. It is shown that the secrecy rate of the proposed scheme scales with the total system powerP0and the number of available relaysKaccording to 1/2log2(1 +P0/8logK) - 1.6 bits/channel use . Although the proposed power allocation and relay selection assume global CSI available, the performance under imperfect relay CSI is also investigated. Also, the performance under distributed relay selection with limited feedback is demonstrated.

We consider a Gaussian wiretap channel model with a single-antenna source, destination and eavesdropper. The communication is assisted by multiple multiantenna helpers that transmit noise to confound the eavesdropper. First, we consider a nulling scheme, in which each helper independently transmits noise, designed to maximize the system secrecy rate while creating no interference to the destination. In this scheme, each helper requires only local relay-destination channel state information (CSI). When global CSI is available at the relays, the nulling scheme is not optimal. The optimal jamming noise structure is also provided under global CSI. Interestingly, it is shown both analytically and via simulations that, despite its reduced CSI requirements, the nulling scheme may have secrecy rate performance that is very close to the optimal one. The probability of outage of the nulling scheme is provided in closed form based on the statistics of the eavesdropper CSI.

A relay network approach is proposed that allows multiple source-destination pairs to communicate simultaneously. The communication is enabled by a multi-antenna amplify-and-forward relay and is implemented in two slots; in the first slot, the sources transmit, and in the second slot, the relay forwards the received signals after processing them through a Zeroforcing Beamforming (ZFBF) matrix. The relay determines the ZFBF matrix so that the transmit power is minimized while the SINR of each destination meets a predefined constraint. Initially, the design is based on perfect channel state information (CSI). Imperfect CSI results in interference at the destinations. This effect can be controlled by increasing the number of relay antennas, or by optimally selecting the antennas to be used. Antenna selection schemes are proposed, which minimize the outage probability, or the destination interference. Also, an iterative scheme is proposed, whereby the amplitudes of the beamforming weights of all source-destination pairs are iteratively increased so that the worst case SINRs meet the requirements of the destinations. It is shown that this approach, coupled with antenna selection, greatly reduces the outage probability.

A wireless relay channel is considered, consisting of a multi-antenna source, a single-antenna destination, a single-antenna eavesdropper and a set of multi-antenna relays (helpers) that act as jammers to the eavesdropper. Each helper knows only its own link to the receiver and independently transmits jamming noise, lying in the null space of its own link to the destination, thus causes no interference to the destination. The source, knowing the main channel explicitly and having statistical information on the eavesdropper channel, designs the input covariance matrix so that the secrecy rate is maximized subject to an outage probability constraint and a sum power constraint. We show that the optimal input covariance matrix has rank one. Assuming that the eavesdropper channels follow a zero-mean Gaussian distribution with known covariance, the outage probability and outage secrecy rate are derived in closed form. Simulation results in support of the analysis are provided.

In widely distributed multiple radar systems, employing larger numbers of transmit and receive antennas supports better target parameter estimation. Increased dimensions results in higher communication needs, synchronization overhead, and processing complexity. In our previous studies, resource-aware operational schemes have been introduced for a given localization estimation mean-square error (MSE) threshold requirement. Power allocation scheme that minimizes the total transmitted power for a given MSE goal has been derived. As most of the transmitted power was allocated to a few of the available transmit antennas, a subset selection scheme has been proposed to identifying a minimal set of transmit and receive antennas that offer the required accuracy performance. The study indicates that some transmit and receive antenna pairs contribute more than others to the localization performance. Based on this, a different approach to resource-aware operation is proposed in this paper. The objective is to identify an antenna subset that offers an optimal tradeoff between performance loss in term of localization MSE and the active subset size. By setting an acceptable loss threshold, relative to the best performances achievable with all antennas active, joint optimization of subset size and power allocation is performed to maximize the trade-off gains. A mixed optimization problem is defined, based on the Cramer-Rao bound (CRB), and fast approximation algorithm is proposed, maximizing the trace of the Fisher information matrix (FIM) while minimizing the number of active antennas. The closed-form expression of the CRB offers additional understanding of the relation between the geometric layout of the transmit and the receive antennas with respect to the target and their relative contribution to the performance.

The paper considers transmit beamforming for physical-layer multicasting in a wireless system consisting of a single transmitter with multiple antennas and multiple single antenna users. The beamforming weights are obtained so that the transmitted power is minimized subject to meeting the minimum signal-to-noise (SNR) requirements of each user. As in previous works, semidefinite programming (SDP) relaxation is considered. The contribution of this paper lies in that it provides effective and convergent algorithms to obtain the solution for the cases in which SDP yields a solution with rank greater than one. Two methods are proposed, namely, the coordinate descent method, which is applicable for the case of at least as many transmit antennas as users, and the p-norm approximation, which is is applicable for general case. Numerical results are presented to illustrate the performance of the proposed algorithms.

A performance driven power allocation scheme is proposed for target localization in widely distributed multiple-radar architectures. For a total transmitted power goal, power may be uniformly allocated among all transmit stations. This will result with a specific target location estimation mean-square error (MSE) that may be evaluated using the Cramer-Rao bound (CRB). However, in the case of target tracking, where previous knowledge of the system exists, uniform allocation might not be the most energy efficient method. In this paper, the following optimization problem is considered: find an optimal power allocation among the transmit radar stations, such that the total transmitted energy is minimized for a given performance objective. To address this problem, the Karush-Kuhn-Tucker (KKT) conditions for the resulting nonconvex optimization problem are formulated and a set of parallel, non-overlapping, optimization problems is derived. This approach supports distributed processing of the optimal power allocation. Additionally, the analytical expressions provide imperative understanding of the relation between the system characteristics and the manner in which power is allocated. It is shown that uniform power allocation is not in general optimal and that considerable power savings may be attained through power adaptation.

Widely distributed multiple radar architectures offer parameter estimation improvement for target localization. For a large number of radars, the achievable localization minimum estimation mean-square error (MSE), with full resource allocation, may extend beyond the system predetermined performance goals. In this paper, a performance driven resource allocation scheme for multiple radar systems is proposed. The number of transmit and receive radars employed in the estimation process is minimized by effectively selecting a subset of active radars such that the required MSE performance threshold is attained. As the goal is to obtain a performance level with the lowest cost, in terms of active system elements, the problem is formulated in a combinatorial optimization framework as a knapsack problem (KP). The Cramer-Rao bound (CRB) is used as a performance metric. Cost parameters, representing operational cost or any other utilization constraints on the radars, are associated with each of the radars. These are incorporated in the KP formulation, as decision making factors in the selection process. Radar subset selection is implemented through a heuristic algorithm, successively selecting radars that minimize the performance gap between the temporal CRB and a given MSE goal. The proposed algorithm offers considerable reduction in computational complexity when compared with an exhaustive search. By minimizing the number of operational radars needed to complete the task, this concept introduces savings in both communication link needs and central processing load, in addition to the operational ones.

Cooperative jamming (CJ) schemes support secure wireless communication in the presence of one or more eavesdroppers. Larger numbers of cooperative relays provide better secrecy rate, while increasing the communication and synchronization needs associated with cooperative beamforming. For low density networks (small number of relays) the secrecy rate changes rapidly with an increase in the number of relays, while for higher density networks increasing the number of relays has significantly smaller effect on the secrecy rate. This research considers a resource-aware approach: instead of using all available relays, choose the smallest set of active relays that meet a predetermined performance goal. The problem is formulated as a knapsack problem that may be solved through an exhaustive search. As this search method has exponential complexity, three heuristic algorithms are proposed, offering significant complexity reduction. The first one relies on the individual relay secrecy rate with a greedy algorithm. The second one is based on the beamforming weights norms, and the third one successively selects relay nodes that minimize the performance gap between the temporal secrecy rate and the given secrecy rate goal. Simulation results demonstrate that relatively high secrecy rate may be achieved with a small number of active relays. The first method performs better for low secrecy rate threshold levels, while the second does so at high threshold. The third method combines the advantages of the previous two and offers performance very close to the optimum at the expense of higher complexity than the first two.

A Gaussian multiple-antenna wiretap channel model is considered, where there exists a transmitter equipped with multiple antennas, a legitimate receiver equipped with a single antenna, and an eavesdropper equipped with multiple antennas. We study the problem of finding the optimal input covariance that maximizes the ergodic secrecy rate subject to a power constraint, assuming that full information on the legitimate channel is available to the transmitter, but only statistical information on the eavesdropper channel is known. More specifically, we investigate the case of MIMO Rician fading for the eavesdropper channel. We show that the optimal input covariance has rank one, which allows us to reduce the original optimization problem to a smooth one variable optimization problem. We propose a Newton-type method for a local maximizer, and Piyavskii's algorithm for the global maximizer. Numerical results are presented to illustrate the proposed algorithms.

A Gaussian multiple-input single-output (MISO) wiretap channel model is considered, where there exists a transmitter equipped with multiple antennas, a legitimate receiver and an eavesdropper, each equipped with a single antenna. We study the problem of finding the optimal input covariance that maximizes the ergodic secrecy rate subject to a power constraint, where only statistical information about the eavesdropper channel is available at the transmitter. This is a non-convex optimization problem that is in general difficult to solve. Existing results address the case in which the eavesdropper or/and legitimate channels have independent and identically distributed Gaussian entries with zero mean and unit variance, i.e., the channels have trivial covariances. This paper addresses the general case in which the eavesdropper and legitimate channels have nontrivial covariances. A set of equations describing the optimal input covariance matrix are proposed along with an algorithm to obtain the solution. Based on this framework, it is shown that when full information on the legitimate channel is available to the transmitter, the optimal input covariance has always rank one. It is also shown that when only statistical information on the legitimate channel is available to the transmitter, the legitimate channel has some general non-trivial covariance and the eavesdropper channel has trivial covariance, the optimal input covariance has the same eigenvectors as the legitimate channel covariance.

The paper considers a network scenario in which multiple source-destination node pairs need to communicate simultaneously. Each source and destination is equipped with one antenna and the communication is assisted by a multi-antenna relay operating in Amplify-and-Forward fashion. The communication occurs in two slots; in the first slot, the sources transmit simultaneously, and in the second slot, the relay retransmits the signals which were received by its antennas during the first slot, after linearly processing them via a Zeroforcing Beamforming (ZFBF) matrix. Two different designs for the ZFBF matrix are proposed. The first design allocates the relay power so that all data streams have the same useful power. The second design adjusts the relay weights for all S-D pairs in a way that maximizes the sumrate. It is shown analytically that, when the source or relay power is high, the proposed sumrate maximization method has the same ergodic sumrate as the cut-set bound with two identical slots and a ZF precoder in the broadcast hop or a ZF equalizer in the multiple access hop, or maintains a constant gap from the cut-set bound with a ZF precoder in the broadcast hop. Although initially perfect channel state information (CSI) is assumed available at the relay, an upper bound of the ergodic sumrate loss caused by relay CSI errors is also derived.

A Gaussian multiple-input single-output (MISO) wiretap channel model is considered, where there exists a transmitter equipped with multiple antennas, a legitimate receiver and an eavesdropper each equipped with a single antenna. We study the problem of finding the optimal input covariance that achieves ergodic secrecy rate subject to a power constraint where the full information on the legitimate channel is known to the transmitter, but only statistical information on the eavesdropper channel is available at the transmitter. Existing results address the case in which the eavesdropper channel has independent and identically distributed Gaussian entries with zero-mean, i.e., the channel has trivial covariance. This paper addresses the general case where eavesdropper channel has nontrivial covariance. A set of equations describing the optimal input covariance matrix are proposed. Based on this framework, we show that the optimal input covariance has always rank one. Numerical results are presented to illustrate the algorithm.

The paper considers a scenario where there are multiple single antenna source-destination (S-D) pairs in the network that need to communicate simultaneously. An amplify and-forward relaying scheme is proposed, that employs single relay equipped with multiple antennas, and is implemented in two steps. In the first step the sources transmit simultaneously, while in the second step the relay retransmits the signals that it receives on its antennas after linearly processing them via a beamforming (BF) matrix. The BF matrix is obtained by maximizing the sumrate of all source-destination pairs subject to a total power constraint at the relay. Since the optimal BF design problem is non-convex and hard to solve, a suboptimal zero-forcing beamforming (ZFBF) design approach is proposed. It is shown that with high source power, or high relay power, such a relaying system achieves either the same sumrate as the cut- set bound with ZF receiver/precoder and timing parameter 1/2, or maintains a constant gap from it, which shows the optimality of the proposed scheme in terms of multiplexing gain.

We consider a multiple-input multiple-output (MIMO) Gaussian wiretap channel model, where there exists a transmitter, a legitimate receiver and an eavesdropper, each node equipped with multiple antennas. We study the problem of finding the optimal input covariance that achieves secrecy capacity subject to a power constraint, which in general leads to a difficult non-convex optimization problem. For the Gaussian multiple-input single-output (MISO) wiretap channel where there exists a transmitter equipped with multiple antennas, a legitimate receiver and an eavesdropper each equipped with one antenna, the optimal input covariance is obtained in closed form. For the general case, we derive the necessary conditions for the optimal input covariance in the form of a set of equations. We show that for MISO case, the derived conditions guarantee the optimal input covariance. Numerical results are presented to illustrate the proposed theoretical findings.

A cross-layer scheme, namely ALOHA with Collision Resolution (ALOHA-CR), is proposed for high throughput wireless communications in a cellular scenario. Transmissions occur in a time-slotted ALOHA-type fashion but with an important difference: simultaneous transmissions of two users can be successful. The physical layer required to achieve this functionality is described and the statistical properties of the user delays are determined so that the probability of user separation is maximized. An implementation of ALOHA-CR on the Wireless Open Access Research Platform (WARP) testbed containing software defined radio nodes is discussed and experimental results are presented.

The paper investigates linear beamforming techniques in relay networks with multiple independent sources, destinations and relay(s). The goal is to determine the beamforming matrix to minimize the sum transmit power at the relays while meeting signal-to-interference (SINR) requirements at the destinations. Two scenarios are considered: one is the case of single relay with multiple antennas in which the beamforming matrix can be of any form, while the other is the case of distributed single antenna relays, where the beamforming matrix is diagonal. An interference zero-forcing criterion is applied for the design of the beamforming matrix at the relays assuming that perfect channel state information is available. It is shown that the former problem is a constrained least square problem and the latter one can be solved by SemiDefinite Programming (SDP) with relaxation. Simulation results show that the proposed beamforming schemes can reliably support multiple parallel data streams with SINR requirements in multiuser relay systems.

This paper proposes a novel radar system, namely step-frequency with compressive sampling (SFR-CS), that achieves high target range and speed resolution using significantly smaller bandwidth than traditional step-frequency radar. This bandwidth reduction is accomplished by employing compressive sampling ideas and exploiting the sparseness of targets in the range-speed space.

A physical layer approach to security for wireless networks is considered. In single-antenna wireless systems, such approaches are hampered by channel conditions in the presence of one or more eavesdroppers. Cooperation has the potential to overcome this problem and improve the security of of wireless communications. In this paper, an amplify-and-forward based cooperative protocol is proposed. Assuming availability of global channel state information, system design that maximizes the secrecy capacity is considered. Since the optimal solution to this problem is intractable, suboptimal closed-form solutions are proposed that optimize bounds on secrecy capacity for the case of a single eavesdropper, or that introduce additional constraints, such as nulling of signals at all eavesdroppers, for the case of multiple eavesdroppers.

Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for direction-of-arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOA of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center for further processing.

Collaborative beamforming enables nodes in a wireless network to transmit a common message over long distances in an energy efficient fashion. However, the process of making available the same message to all collaborating nodes introduces delays. The authors recently proposed a MAC-PHY cross-layer scheme that enables collaborative beamforming with significantly reduced collaboration overhead. The method requires knowledge of node locations and internode channel coefficients. In this paper, the performance of that approach is studied analytically in terms of average beampattern and symbol error probability (SEP) under realistic conditions, i.e., when imperfect channel estimates are used and when there are phase errors in the contributions of the collaborating nodes at the receiver.

We consider a wireless system where users are equipped with one antenna each, and the antennas are spatially distributed and not physically connected. Multiple users transmit simultaneously narrowband signals using the same carrier frequency, each one using a distinct pulse shaping function with fixed bandwidth and time duration. We show that the diversity provided by the different pulse shaping functions as well as user delays and carrier frequency offsets between transmit antennas and receiver enables user separation and recovery of transmitted signals in a blind fashion, i.e., without the need for transmission of pilot symbols. The received base-band signal is over-sampled and its polyphase components are viewed as the outputs of a virtual MIMO system. The user signals are recovered through blind MIMO system estimation and equalization.

Via collaborative beamforming, nodes in a wireless network are able to transmit a common message over long distances in an energy efficient fashion. However, the process of making available the same message to all collaborating nodes introduces delays. In this paper, a MAC-PHY cross-layer scheme is proposed that enables collaborative beamforming at significantly reduced collaboration overhead. It consists of two phases. In the first phase, nodes transmit locally in a random access time-slotted fashion. Simultaneous transmissions from multiple source nodes are viewed as linear mixtures of all transmitted packets. In the second phase, a set of collaborating nodes, acting as a distributed antenna system, beamform the received analog waveform to one or more faraway destinations. This step requires multiplication of the received analog waveform by a complex weight, which is independently computed by each cooperating node, and which allows packets bound to the same destination to add coherently at the destination node. Assuming that each node has access to location information, the proposed scheme can achieve high throughput, which in certain cases exceeds one. An analysis of the symbol error probability corresponding to the proposed scheme is provided.

A cooperative random access protocol, namely ALLIANCES, was recently proposed for resolving collisions in wireless networks. In (Dong, L, et al., 2006), we proposed an multichannel extension of ALLIANCES that in addition to cooperation diversity can exploit multipath diversity, and thus improve throughput at high traffic load and reduce packet delays. In this paper, we propose an improvement on (Dong, L, et al., 2006) that makes more efficient use of available bandwidth and thus can achieve high throughput at all traffic loads. Furthermore, we present analytic performance characterization that provides insight on the relationship between achievable diversity and parameters like collision order, number of relays, channel length and number of carriers per subchannel.

ALLIANCES is a recently proposed cooperative random access protocol for wireless networks. In this paper we modify the original model to include user location information. We also derive pair-wise error probability (PEP) under Rayleigh flat fading channel and a power-law attenuation environment. Based on the PEP analysis we propose an optimal relay selection scheme, which achieves significant throughput gains as compared to the random relay selection scheme in the original ALLIANCES.

Multiple carrier-frequency offsets (CFO) arise in a distributed antenna system, where data are transmitted simultaneously from multiple antennas. In such systems the received signal contains multiple CFOs due to mismatch between the local oscillators of transmitters and receiver. This results in a time-varying rotation of the data constellation, which needs to be compensated for at the receiver before symbol recovery. This paper proposes a new approach for blind CFO estimation and symbol recovery. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual multiple-input multiple-output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response is estimated and used to subsequently transform the multiple CFOs estimation problem into many independent single CFO estimation problems. Furthermore, an initial estimate of the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao lower bound is also derived, and the large sample performance of the proposed estimator is compared to the bound.

Performance of wireless communications systems is degraded by fading, typically combated via spatial diversity by multiple transmit and receiver antennas. Practical limitations may limit their use; thus, cooperative transmissions have been proposed to introduce diversity without the need for multiple antennas. We propose a novel cooperative scheme that performs distributed coding of user data. It needs each user transmit on one single channel and allows large flexibility between rate and diversity. A trade-off is increased complexity as more users are required to be decoded simultaneously by the base station. We present construction of the distributed code for different choices of rates and diversity orders by means of simple linear block codes, resulting in reduced complexity for decoding operations. Achieved diversity order is bounded by the minimum distance of the implemented distributed code. Performance of the scheme is studied via analytical bounds and numerical simulations.

A random access protocol for wireless networks was recently proposed that by exploiting cooperation of network nodes can resolve collisions and thus achieve high throughput. In this paper we propose a multichannel extension of that approach that can lead to throughput improvement and significantly reduce packet delays at high traffic loads, while at the same time can handle frequency selective fading. The channel is divided into separable subchannels and each user can transmit packets over multiple subchannels. We propose schemes for resolving collisions on the various subchannels in a way that minimizes the average processing time for each collided packet. At the physical layer we propose an OFDM approach, where the subchannels are groups of carriers

A random access protocol for wireless networks was recently proposed that via cooperation of network nodes can resolve collisions and thus achieve high throughput. In this paper, we provide analytical expressions for the Bit-Error-Rate (BER) performance of that scheme in a Rayleigh flat fading scenario. Our analysis indicates that the spatial diversity introduced by user cooperation enables lower BER than non-cooperative protocols that avoid collisions, such as ALOHA or TDMA. The BER performance shows that the cooperative random access protocol is best suited for variable rate traffic. The analytical results are validated via simulations.

We consider the problem of frequency domain identification of a convolutive multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed in [1] that uses PARAFAC decomposition of a tensor that is formed based on third-order statistics of the system output. The approach of [1] utilizes only one slice of the output tensor to recover one row of the system response matrix. We here propose an approach that fully exploits the information in the output tensor. As a result, the proposed method not only achieves lower error values but also becomes applicable to MIMO systems with possibly more inputs than outputs. By combing two output tensors, we can make the approach applicable to more systems. We also extend the method to employ fourth-order statistics of the system output.

We propose a novel high-throughput medium access scheme for wireless networks that is suitable for bursty sources. We view the wireless network as a spatially distributed antenna, with antenna elements linked via the wireless channel. When there is a collision, the packets involved in the collision are saved in a buffer. In the slots following the collision, a set of nodes, designated as relays, form an alliance and bounce off the signal that they received during the collision slot. By processing the originally collided packets and the signals forwarded by the relays, the destination node can formulate and solve a multiple-input multiple-output problem, the inputs of which are the original packets. The spatial diversity introduced via the cooperative relaying enables us to deal with the wireless channel effectively without any bandwidth expansion nor additional antenna hardware. The proposed scheme maintains the benefits of ALOHA systems in the sense that all nodes share access to media resources efficiently and without extra scheduling overhead, and enables efficient use of network power.

We consider the problem of MIMO channel estimation subject to a given error and delay constraints. Our objective is to minimize the energy spent during the channel estimation phase, which includes transmission of training symbols, storage of those symbols at the receiver, and also channel estimation at the receiver. We develop a model that is independent of the hardware or software used for channel estimation, and use a divide and conquer strategy to minimize the overall energy consumption.

It has been well established by now that high-speed wireline traffic exhibits self-similar behavior. Several studies in the past have hypothesized that wireless traffic is also self-similar but without adequate justification. In this paper we study the propagation of self-similarity as self-similar wireline traffic feeds to a gateway that interconnects a wireline to a wireless network. We model the wireline traffic as an on/off process. We propose models for buffering and repacking performed at the gateway. Based on those models and also statistical models for the wireless channel, we study the statistics of the outgoing on/off traffic. We show that when the on and off state durations of the input traffic are both heavy-tail distributed, such as is the case in LAN traffic, the outgoing traffic is self-similar. On the other hand, if the on state durations are heavy-tail distributed but the off state durations have finite variance, such as in variable-bit-rate video traffic, the self-similarity may disappear if the gateway has a buffer much larger than the maximum channel capacity and it operates under an energy conserving protocol.

We consider a single user OFDM system that experiences time-varying and frequency selective channels. We study a training based scenario, where the channel is estimated based on pilots that proceed the transmission of the information bearing blocks, and then used to equalize the subsequent data blocks. In such a scheme, due to the time-varying channel, the BER increases as the index of the data block increases. Considering commonly used models for time-varying channels, we derive an analytical expression for the bit error rate as a function of the block index. The proposed analysis is important in optimizing the system performance, as it can be exploited at the transmitter to determine how often pilots need to be transmitted, or what kind of modulation should be used on each carrier in order to maintain a certain error level.

We consider the linearly precoded OFDM approach proposed by A.P. Petropulu et al. (see IEEE Trans. Wireless Commun., 2003), where a non-redundant precoding was applied to the symbol blocks before entering the OFDM system. The precoding, while it maintained the transmit power, introduced a structure to the transmitted signal that allowed for blind channel estimation by a simple auto-correlation performed at the receiver. We propose an adaptive modulation based extension of the method of Petropulu et al. in order to combat a channel with deep fading. Bits are allocated on each subcarrier so that the overall transmit power is minimized under a fixed bit error rate (BER). The obtained bit allocation can also be viewed as minimizing BER for the precoded system, under a fixed overall transmit power constraint. The proposed approach provides large performance gains over the uniformly loaded one, especially under deep fading conditions, for the same overall throughput and transmit power.

It has been well established that high-speed wireline traffic exhibits self-similar behavior. We study the propagation of self-similarity as wireline traffic is sent through a gateway to a wireless network. We employ a commonly used model for buffering and repacking performed at the gateway, and study the statistics of the output traffic. Both analysis and simulations reveal that the buffer system can produce traffic that has different degrees of self-similarity as compared to the incoming traffic, or even traffic that is no longer self-similar.

Performance of real-time applications on end-to-end packet channels are strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and present a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modeled by a hidden Markov model with appropriate hidden variables that capture the current state of the network. In this paper we propose an input/output hidden Markov model that, trained with a modified version of the expectation-maximization algorithm, shows excellent performance in modeling typical channel behaviors in a set of real packet links. The work extends to case of variable inter-departure time the previous proposed hidden Markov model that well characterizes losses and delays of packets from a periodic source.

A novel approach of blind channel estimation for orthogonal frequency-division multiplexing (OFDM) systems is proposed. A linear transformation is applied on each block before it enters the OFDM system. The transform imposes a correlation structure on the transmitted blocks, which is exploited at the receiver to recover the channel via simple cross-correlation operations. The proposed approach is computationally simple and converges fast, which makes it a good candidate for estimation of fast-varying channels. Its performance is tested analytically, through a mean-square error analysis, and also via simulations. Results show that it compares favorably to the training-based scheme used in the IEEE 802.11a wireless standard.

We consider the problem of blind estimation of a band-limited channel excited by a cyclostationary input. We propose a channel estimation method that, like several existing ones, exploits the relationship between the channel frequency response and the cyclic spectrum. However, the novelty here is an approximation made for the discretized phase of the cyclic spectrum, which, under certain conditions, results in a significant simplification of the aforementioned relationship. The result is a channel estimation method with complexity equal to that of an IDFT. The proposed approach is applied to simulated data, and real recordings obtained at our wireless communications testbed, and is compared to existing methods.

The EAFRP model has been recently proposed for modeling the self-similar and impulsive traffic of high-speed networks. For mathematical simplicity, it assumes that the available transmission bandwidth in the network is infinite. We here propose a modification of the model that takes into account the fact that the network has a limit, R, on the total traffic rate through it, and in addition, each user's traffic rate is often independently limited to a value, L, which is significantly lower than the network's limit (L

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A feature fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate A(z) and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A(z) of 0.96 with a confidence interval of at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

We consider a general n × n MIMO system excited by unobservable inputs that are spatially independent, cyclostationary with unknown statistics. We provide a set of conditions under which the system is uniquely identifiable based on second-order frequency-domain correlations of the system output. Such a MIMO problem appears in many applications, such as multi-user communications and separation of competing speakers.

We address the problem of blind identification of a convolutive Multiple-Input Multiple-Output (MIMO) system with more inputs than outputs, and in particular, the 3-input 2-output case. We assume that the inputs are temporally white, non-Gaussian distributed, spatially independent and that the system impulse response can be complex. In this paper, we look at the problem in the frequency domain, where, for each frequency we construct two tensors based on cross-polyspectra of the output. These tensors lead to the system frequency response within frequency dependent scaling and permutation ambiguities. We propose ways to resolve these ambiguities, and show that it is possible to obtain the system response within a scalar and a linear phase.

We consider a multi-user wireless communication scenario, where signal reception is often corrupted by interference from co-channel users. Under the assumptions that the path loss between the source and destination increases with distance in a power-law fashion, and that during each symbol interval the interferers form a Poisson point processes in space, which are independent between different symbols, it has been shown in the past that interference samples, obtained at symbol rate, constitute an i.i.d, α-stable process. The latter independence assumption, however, is unrealistic as it implies that the session life of each interferer is one symbol interval long only. In this paper we let the session life of each interferer be a random variable. We show that the resulting interference is non i.i.d. and derive its joint statistics. As a special case, a heavy-tail distributed session life results in long-range dependent interference. We validate our claims via simulations, and demonstrate the importance of taking into account the dependence of the interference when performing signal detection.

This paper addresses the problem of blind separation of convolutive mixtures via contrast maximization. New frequency-domain contrast functions are constructed based on second and higher-order spectra of the observations. They allow one to separate mixtures of sources which are spatially independent, and temporally possibly non i.i.d. linear or non-linear processes. The proposed criteria provide a framework for extending to the convolutive case contrasts that have been proposed in the context of instantaneous mixtures.

Traffic flow in high-speed data network systems is often impulsive and long-range dependent. Impulsiveness implies a heavy-tailed marginal distribution, thus lack of finite second-order statistics. Hence, traditional methods for quantifying the long-range dependence of traffic based on its second-order statistics are not applicable. Long-range dependence and self-similarity play an important role in traffic engineering. We have recently shown that the generalized codifference can quantify the dependence structure of impulsive self-similar processes, such as high-speed network traffic. We propose an estimator for the generalized codifference and provide the conditions for it to be asymptotically consistent. We show that these conditions are satisfied for the EAFRP which is a process proposed for modeling high-speed network traffic. We provide simulation results to demonstrate the properties of the proposed estimator, and show how it can be a useful tool in maintaining fairness among users sharing limited network resources.

We propose a separable decomposition approximating the Karhunen-Loeve transform for random fields. We show that this problem is related to a joint singular value decomposition of a set of matrices and we provide an efficient algorithm to compute it. Finally, we illustrate the interest of this new tool for image representation and approximation.

In this paper we propose a novel frequency domain approach for the n/spl times/n MIMO identification problem. The proposed method is a subspace approach that estimates the parameters of a FIR MIMO system, driven by temporally i.i.d. and spatially independent non-Gaussian processes, using cross poly-spectra of any order and cross-power spectra of the system output. Extensions to the case of non-white input signals are also discussed. Simulation results are provided to illustrate the performance of the new algorithm.

We model the radio-frequency (RF) ultrasound echo by a shot noise process with narrow-band power-law filter function. As a consequence, the in-phase and quadrature components of the return signal are shown to exhibit 1/f/sup /spl beta// type spectral behavior. The envelope also exhibits this type of spectral behavior but with a different exponent. The model parameters, namely the rate of the point process and the power-law exponent, are related to tissue density and attenuation, respectively. Since these tissue characteristics change due to disease, estimates of the model parameters are investigated as potential tissue characterization features. We validate our claims based on clinical ultrasound images.

In this paper we propose a frequency domain approach for the separation of convolutive mixtures based on higher order statistics only. The system frequency response is first obtained up to a diagonal phase ambiguity matrix based on generalized eigen-decomposition of two cross-trispectrum matrices. Then the phase ambiguity is removed by exploiting different slices of the cross-trispectrum.

The blind estimation of mixing channels resulting from frequency selective fading and multipath in a multi-user CDMA system is an important problem in wireless communications. We present a novel frequency-domain approach using second order spectral statistics for recovering the unknown channels. Unlike other methods which are based on time-domain analysis we make no particular assumption about the support of the mixing channels except that they have finite length (FIR). The method is based on the fact that the source sequences obtain a known spectral color derived from the corresponding spreading code used in CDMA.

We treat the blind identification problem for a n/spl times/n MIMO system using second order frequency-domain statistics and asymmetric PCA neural models. It is assumed that the source signals are colored, stationary, and pair-wise independent sequences with otherwise unknown statistics. We introduce a set of invariant indices that are used to tackle the problem of frequency-dependent ambiguities in the ordering and the phase of the retrieved singular vectors. For the case of 2/spl times/2 systems we present a complete identification procedure based on the corresponding invariant indices and we conjecture that these indices can be instrumental in the solution of the general n/spl times/n problem as well.

Although the reconstruction of a nonminimum-phase system excited by a stationary non-Gaussian white input is only possible using higher-order statistics (HOS) of the system output, there has been a lot of criticism in the literature against the amount of data required for keeping estimation errors low, and the complexity involved. Several attempts for reducing the variance of the HOS estimates have appeared. In the case of bandlimited signals, we have demonstrated via simulations that the estimation variance can be reduced if "good" slices, instead of the whole bispectrum, are used. This suggests a potential reduction of the variance in the system estimates, without having to resort to long observations. We justify theoretically the dependence of the system estimate variance on the bispectrum slice, and the criterion of slice selection. We also present simulation results, where the selected-slices approach appears to result in much lower estimation variance, as compared to other entire-bispectrum based approaches, for data lengths as low as 64 samples.

1/f/sup /spl beta//-type spectral behavior has received considerable attention in the past few years because it arises from a wide range of natural phenomena. By expressing a 1/f/sup /spl beta// process as a fractional integral of white noise, we show that, if /spl beta/1, the process has stationary /spl alpha/-stable increments. We also provide closed form expressions for the relationship between /spl beta/ and /spl alpha/. The theoretical results are verified via real ultrasound data. Ultrasound breast data, or their increments, which appear to be 1/f/sup /spl beta//, are shown to follow reasonably well the /spl alpha/-stable model.

We propose a method for the reconstruction of a complex signal from its Fourier phase only, where the phase is known within a linear phase term, and the sequence's length is unknown. The case of the phase known exactly has received a lot of attention in the past, however, in most cases the phase can be estimated up to a linear phase term whose slope is unknown. Moreover, in most cases of interest, the exact length of the sequence which is to be recovered is unknown. As an application of the reconstruction from phase technique, we propose a method for blind channel identification.

We present a blind deconvolution-based approach for the restoration of speech degraded by the acoustic environment. The proposed scheme processes the outputs of two microphones using cepstra operations and the theory of signal reconstruction from phase only. Under mild assumptions, it reconstructs the room impulse response associated with each microphone and restores the speech signal.

The authors model tissue as a collection of point scatterers embedded in a uniform media, and show that the higher-order statistics (HOS) of the scatterer spacing distribution can be estimated from digitized radio frequency (RF) scan line segments and be used in obtaining tissue signatures. The authors assume that RF echoes are non-Gaussian, on the grounds of empirical/theoretical justifications presented in the literature. Based on their model for tissue microstructure, the authors develop schemes for the estimation of reasonable periodicity as well as correlations among nonperiodic scatterers, Using HOS of the scattered signal, the authors define as tissue "color" a quantity that describes the scatterer spatial correlations, show how to evaluate it from the higher-order correlations of the digitized RF scan line segments, and investigate its potential as a tissue signature. The tools employed, i.e., HOS, were chosen as the most appropriate ones because they suppress Gaussian processes, such as the one arising from the diffused scatterers. HOS, unlike second-order statistics, also preserve the Fourier-phase of the signature, the color of the tissue response. Working on simulated and clinical data, the authors show that the proposed periodicity estimation technique is superior to the widely used power spectrum and cepstrum techniques in terms of the accuracy of estimations. The authors also show that even when there is no significant periodicity in data, they are still able to characterize tissues using signatures based on the higher-order cumulant structure of the scatterer spacing distribution.

We propose an algorithm for the restoration of speech that has been degraded through addition of multiple echoes. The proposed scheme processes the outputs of two microphones using cepstra operations and the theory of signal reconstruction from phase only. Under mild assumptions it reconstructs the room impulse response associated with each microphone and restores the speech signal. We demonstrate the performance of the proposed scheme using speech reverberated by simulated room acoustics.>

A scheme for the detection of transient signals with unknown waveforms and arrival times is presented. The detection is performed in the discrete wavelet transform (DWT) domain, where the presence of a transient is indicated by a peak at a location that depends on the values of the dilation and translation parameters. By choosing these parameters appropriately one can control the sharpness of this peak as well as the distance between different peaks that correspond to different arrival times. As a result, transients that partially overlap in time can be resolved in the DWT domain. The DWT domain preserves the arrival time information even when the transient is corrupted by additive zero-mean noise at a very low signal-to-noise ratio.>

The authors present a blind deconvolution scheme for the reconstruction of a signal that propagates in a multipath environment in the presence of additive zero-mean Gaussian noise. Two receivers are placed to record the transmitted signal convolved with a different channel for each receiver, embedded in noise. The recorded signals are transformed in the bicepstrum domain where the additive zero-mean Gaussian noise is suppressed, and then the differences of their minimum phase cepstra coefficients and the differences of their maximum phase cepstra coefficients are computed. These differences correspond to the Fourier phases of two FIR (finite impulse response) sequences that can be reconstructed out of phase information only, as long as the transmission channels are FIR and they have no zeros on the unit circle. The reconstruction of these sequences leads to the computation of the minimum and maximum phase cepstra coefficients of the two channels, and subsequently the cepstra coefficients of the transmitted signal can be computed and combined to reconstruct the signal itself.>

An iterative algorithm for reconstructing a finite impulse response (FIR) signal from only the phase of its bispectrum is introduced. The algorithm is based on the key observation that the differences of the bicepstrum coefficients contain all the information concerning the phase of the signal, whereas their sums contain the magnitude information. Analysis and simulation examples are presented to demonstrate the algorithm's convergence properties. It is also demonstrated that imposing an energy constraint causes the convergence rate of the algorithm to improve dramatically.>

The purpose of this study is to present an analytic performance evaluation of the complex cepstrum and bicepstrum (i.e. cepstrum of the bispectrum) methods by providing explicit expressions of the bias and variance of cepstrum parameters. A model consisting of a deterministic signal in additive white Gaussian noise and finite length data is assumed. The authors compare a special case of these results and find the range of SNR in which they expect the bicepstrum method to perform better than the complex cepstrum method.>

The authors address the problem in which time delays are closely-spaced, i.e. the distance between two consecutive time delays is significantly less than the duration of the autocorrelation of the FM signal. A high-resolution estimation method using the cumulant cepstra (polycepstra) of the received sensor data as the basic tool for reconstruction is introduced. The effectiveness of the method is demonstrated for different noise conditions and lengths of data. The results apply to sonar signal processing problems in which the acoustic FM signal is embedded in reverberation noise due to the presence of multipath and observation noise.>