Orthogonal Frequency Division Multiplexing (OFDM) has become a very popular technique for digital data transmission on multipath fading channels due to its low computational complexity and simple equalization process. However, the multipath component of these types of channels causes a phenomenon known as frequency selective fading. This type of fading can severely degrade or completely eliminate the signal energy of many of the OFDM tones producing an irreducible error rate, even when no noise is present. Consequently, most OFDM systems operating in multipath fading environments utilize some form of forward error correction (FEC) and block interleaving. OFDM waveforms which utilize FEC are usually referred to as coded OFDM (COFDM). One of the main drawbacks of OFDM and COFDM waveforms is the very large peak power to average power ratio (PAR) which requires the use of very linear power amplifiers (PA) and a large power back-off into the PA. In recent years there has been much interest in creating constant-envelope variations of OFDM and COFDM waveforms in order to overcome the PAR drawback. This paper will investigate constant-envelope (CE) variants of OFDM and COFDM waveforms for use on HF multipath fading channels.

In this paper, an interference cancellation and equalization method is proposed for a multiple-input, multipleoutput orthogonal frequency division multiplexing (MIMO-OFDM) system. Standard equalization techniques such as zero-forcing (ZF) and adaptive array processing techniques such as per-tone or time-domain equalization may require large amounts of training and memory for storage of the weight coefficients. In a MIMO-OFDM system with N carriers, equalizers at each antenna are characterized by the solution space associated with an underdetermined system of linear equations. A structured per-tone technique is proposed that utilizes the extra degrees of freedom in order to cancel interfering signals; the resultant equalizers (weight vectors) have lengths significantly smaller than N, but at least as long as the channel impulse response.

Orthogonal Frequency Division Multiplexing (OFDM) is a popular technique used to combat frequency selective fading in multipath channels. When spectral nulls are present in the channel, they can severely degrade or cancel out several OFDM tones, resulting in an irreducible error rate. Code-Spreaded OFDM (CS-OFDM) combines the characteristics of OFDM and Code Division Multiple Access (CDMA) to create a more robust modulation scheme which provides substantial performance improvements relative to standard OFDM. In CS-OFDM, each sinewave carries a weighted sum of all the information symbols being transmitted in an OFDM block interval. In this paper, an MMSE estimator is derived for each symbol for a number of cases, including when the number of carriers is greater than the number of symbols. The performance of CS-OFDM is analyzed and shown to provide substantial improvements relative to standard OFDM, especially in the latter case.

In active sensing systems such as radar and sensor networks, one is interested in transmitting waveforms that possess an ideal thumbtack shaped ambiguity function. However, the synthesis of waveforms with the desired ambiguity function is a difficult problem in applied mathematics and more often than not, one needs to rely on developing waveforms with an ambiguity function that is close to the desired ambiguity function in some sense. Designing waveforms with ambiguity functions that possess certain desirable properties has been a well researched problem in the field of signal analysis. In this paper, we present a methodology for designing multiantenna adaptive waveforms with autocorrelation functions that allow perfect separation at the receiver. We focus on the 4×4 case and derive the conditions that the four waveforms must satisfy in order to achieve perfect separation. Using these conditions, we show that waveforms constructed using Golay complementary sequences, barker codes and quarter-band signals through kronecker products satisfy these conditions and are therefore seperable at the receiver. We also give examples of more general wavefom families that are matched to the environment and also of waveforms that do not necessarily satisfy the conditions for perfect separation but still have good delay-Doppler ambiguity functions making them suitable for sensing environments.

High Frequency (HF) radio communication channels provide unique challenges to digital communication systems. Typical HF communication systems propagate electromagnetic energy in the 2-30MHz radio spectrum using the earth's ionosphere and surface as both refractors and reflectors for non line of sight communications. Constantly changing ionosphere conditions result in multipath and severe fading channel characteristics. Through numerical simulation, short block length block length (9e+3) Low Density Parity Check (LDPC) forward error correction codes used in conjunction with Orthogonal Frequency Division Multiplexing (OFDM) will be shown to provide excellent communication performance across the HF channel. Average bit error rate performance results will be shown for a 2ms, 1Hz and 2Hz Watterson HF channel model for both regular and irregular LDPC parity check matrices. Some results will also be shown for imperfect channel estimation and its effects upon the performance of the LDPC decoder.

RF (radio-frequency) tags have drawn increasing research interest because of their great potential uses in many radio frequency identification applications. They can also be configured to work with radar as a communication channel by receiving radar acquisition signals, suitably coding these, and retransmitting them back to the radar. This paper proposes a system model for the communication between a noise radar and multiple RF tags. The radar interrogates the RF tags in a region of interest by sending ultrawideband noise signals. Upon receiving the radar's signal, all the tags within the radar's range wake up, and respond to the radar with simple messages. The RF tag filters the radar signal to a unique spectral band, which represents its identification information, and different RF tags occupy different non-overlapping bands of the spectrum of the radar signal. Tag messages are modulated onto the waveform through taps of weighted delays. The radar decodes the RF tag identifications and corresponding messages by cross-correlating the RF tag returned signals with the replica of the radar transmitted signal. Calculations and simulation results both show that the proposed system is capable of communicating simple messages between RF tags and radar.

This paper details the Wireless at Virginia Tech Center for Wireless Telecommunications' (CWT) design and implementation of its Smart Radio (SR) communication platform. The CWT SR can identify available spectrum within a pre-defined band, rendezvous with an intended receiver, and transmit voice and data using a selected quality of service (QoS). This system builds upon previous cognitive technologies developed by CWT for the public safety community, with the goal of providing a prototype mobile communications package for military and public safety First Responders. A master control (MC) enables spectrum awareness by characterizing the radio environment with a power spectrum sensor and an innovative signal detection and classification module. The MC also enables spectrum and signal memory by storing sensor results in a knowledge database. By utilizing a family radio service (FRS) waveform database, the CWT SR can create a new communication link on any designated FRS channel frequency using FM, BPSK, QPSK, or 8PSK modulations. With FM, it supports analog voice communications with legacy hand-held FRS radios. With digital modulations, it supports IP data services, including a CWT developed CVSD-based VoIP protocol. The CWT SR coordinates spectrum sharing between analog primary users and digital secondary users by applying a simple but effective channel-change protocol. It also demonstrates a novel rendezvous protocol to facilitate the detection and initialization of communications links with neighboring SR nodes through the transmission of frequency-hopped rendezvous beacons. By leveraging the GNU Radio toolkit, writing key modules entirely in Python, and utilizing the USRP hardware front-end, the CWT SR provides a dynamic spectrum test bed for future smart and cognitive radio research.

Ultra-WideBand (UWB) communication is an emerging technology with many applications in short range broadcasting such as wireless home entertainment, accurate positioning, etc. Digital implementation of UWB radio is very challenging task as data are processed at very high rates, about 500Mbs or more, which is beyond the capabilities of modern digital signal processors. Thus, computationally cost efficient techniques should be developed to enable digital implementations. This paper presents a complexity reduction method in the digital baseband of receivers for pulsed ultra wide band (UWB) communication. In such receivers incoming signals are correlated with local pulses (masks) for data recovery. We observed that the quantization techniques preserving mask tales have a bit error rate (BER) performances very close to nonquantized version. A simple tale preserving mask quantizer has non-uniformly spaced power-of-two levels for multiplier free correlations. Such a simple tales preservation technique drastically reduced complexity as only shifts, additions and substractions are used in correlations.

Border surveillance applications require low false alarm rates and long endurance. These requirements have not changed since unattended ground sensors (UGS) were first used to monitor Viet Cong activity along the Ho Chi Minh Trail in the 1960's. However the targets are quite different today. Then the targets of interest were large military vehicles with strong acoustic, seismic and magnetic signatures. Currently, the requirements imposed by new terrorist threats and illegal border crossings have changed the emphasis to the monitoring of light vehicles and foot traffic. Unlike with military driven requirements cost of ownership and ease of employment are at least as critical as sensor performance.

Hybrid Continuous Phase Modulation (HCPM) is a variant of Continuous Phase Modulation that is optimized for low-power, wireless communications applications. Hybrid CPM uses either additional Amplitude or Phase pulses to create a higher order modulation without the increase in trellis complexity that normally accompanies higher order CPM modulation schemes. For standard CPM, an increase in the modulation order results in the increase of the symbol alphabet and a corresponding increase in the transmit bandwidth and exponential increase in the complexity of the decoder trellis structure. Hybrid CPM however achieves higher order modulation by adding parallel branches to the base CPM trellis structure thus reducing the receiver complexity. The novelty of this paper is the application of the techniques devised to reduce the demodulator complexity of standard modulation types like PSK and CPM and apply them to Hybrid CPM using a method that would not result in the loss of performance or require any sort of compromise. As an example, this paper provides an analysis of the power and spectral efficiency of the two hybrid CPM waveforms and gives specific examples of the application of reduced state techniques. Both set-partitioning and reduced-state sequence estimation with decision feedback techniques are analyzed and compared. The results will demonstrate that reduced-state sequence estimation can be coupled with Hybrid CPM demodulation without any loss in bit error rate performance.

In this paper, we propose a graph-based method for distributed event-region detection in a wireless sensor network (WSN). The proposed method is developed by exploiting the fact that the true events at geographically neighboring sensors have a statistical dependency in an event-region detection scenario. This spatial dependence amongst the sensors is modeled using graphical models (GMs) and serves as a regularization term to enhance the detection accuracy. The method involves solving a linear system of equations, which can be readily implemented in a distributed fashion. Numerical results are presented to illustrate the performance of our proposed approach.

Security is of critical importance for many potential applications of wireless sensor networks. In order to maintain secure communication throughout the network, it is of vital importance to maintain encryption key freshness by regularly distributing new keys to all nodes. Distribution of group keys used to encrypt broadcast communication is expensive, as it is generally achieved via flooding, which taxes the limited battery life available to each node. We propose LKDT, a lightweight encryption key distribution tree building mechanism with an optional Multi- Coverage Reduction (MCR) stage to provide a framework by which to distribute keys while reducing power consumption and broadcast coverage overlap. Additionally, LKDT can configure itself quickly, allowing the base station to begin updating keys shortly after deployment.

Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems, by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions are time-critical.

The physical safety and well being of the soldiers in a battlefield is the highest priority of Incident Commanders. Currently, the ability to track and monitor soldiers rely on visual and verbal communication which can be somewhat limited in scenarios where the soldiers are deployed inside buildings and enclosed areas that are out of visual range of the commanders. Also, the need for being stealth can often prevent a battling soldier to send verbal clues to a commander about his or her physical well being. Sensor technologies can remotely provide various data about the soldiers including physiological monitoring and personal alert safety system functionality. This paper presents a networked sensing solution in which a body area wireless network of multi-modal sensors can monitor the body movement and other physiological parameters for statistical identification of a soldier's body posture, which can then be indicative of the physical conditions and safety alerts of the soldier in question. The specific concept is to leverage on-body proximity sensing and a Hidden Markov Model (HMM) based mechanism that can be applied for stochastic identification of human body postures using a wearable sensor network. The key idea is to collect relative proximity information between wireless sensors that are strategically placed over a subject's body to monitor the relative movements of the body segments, and then to process that using HMM in order to identify the subject's body postures. The key novelty of this approach is a departure from the traditional accelerometry based approaches in which the individual body segment movements, rather than their relative proximity, is used for activity monitoring and posture detection. Through experiments with body mounted sensors we demonstrate that while the accelerometry based approaches can be used for differentiating activity intensive postures such as walking and running, they are not very effective for identification and differentiation between low activity postures such as sitting and standing. We develop a wearable sensor network that monitors relative proximity using Radio Signal Strength indication (RSSI), and then construct a HMM system for posture identification in the presence of sensing errors. Controlled experiments using human subjects were carried out for evaluating the accuracy of the HMM identified postures compared to a naïve threshold based mechanism, and its variations over different human subjects. A large spectrum of target human postures, including lie down, sit (straight and reclined), stand, walk, run, sprint and stair climbing, are used for validating the proposed system.

Unattended ground sensor systems (UGS) have become an important part of a covert monitoring arsenal in operations throughout the world. With the increased use of unattended ground sensor systems, there is a need to develop communication architectures that allow the systems to have simple emplacement procedures, have a long mission life, and be difficult to detect. Current ad-hoc networking schemes use either a network beacon, extensive preambles, or guaranteed time synchronization to achieve reliable communications. When used in wireless sensor systems many of these schemes waste power through unnecessary transmissions. These schemes compromise the covert nature of UGS through excess transmissions for a non-beaconed network or the periodic beaconing in a beaconed network. These factors are detrimental to sensor systems, which chiefly rely on being covert and low-power. This paper discusses a nonarbitrated, non-GPS synchronized, beaconless approach to discovering, joining, and reliably transmitting and receiving in a low-power ad-hoc wireless sensor network. This solution is capable of performing network discovery upon demand to get an initial alignment with other nodes in the network. Once aligned, end points maintain alignment and can predict when other nodes will be available to listen.

Directional antennas have shown to be advantageous in an ad hoc wireless network over traditional omnidirectional antennas to provide higher network throughput because they can reduce the power required for the service coverage and packet transmission, mitigate interference in the directions away from that of the desired users, and reduce the number of hops between distant source and sink nodes. Recently, multibeam antennas (MBAs) have been developed to further increase the network throughput. MBAs can be implemented using either multiple fixed-beam directional antennas or multi-channel smart antennas. The difference between them lies in whether the beams are predefined or adaptively controlled. MBAs not only inherit the advantages of directional antennas, but also support concurrent communications with multiple neighboring nodes. Such advantages are achieved, however, only with sophisticated scheduling schemes. For example, it is crucial to appropriately allocate the available beams to the neighboring users that attempt to transmit packets. Previous results have shown the importance of utilizing appropriate scheduling in avoiding collision. Specifically, in a multipath propagation environment, signals transmitted from a neighboring node may fall into multiple beams at the receiving node and thus result in more frequent contentions. In this paper, we examine the feasibility and node throughput performance of relevant scheduling algorithms, such as those based on packet priority and throughput maximization, and investigate the effect of multipath propagation on the exploitation of the scheduling schemes as well as on the throughput performance.

During the past five years McQ has been actively pursuing integrating and applying wireless mesh network radios as a communications solution for unattended ground sensor (UGS) systems. This effort has been rewarded with limited levels of success and has ultimately resulted in a corporate position regarding the use of mesh network radios for UGS systems. A discussion into the background of the effort, the challenges of implementing commercial off-the-shelf (COTS) mesh radios with UGSs, the tradeoffs involved, and an overview of the future direction is presented.

Multiuser multiple-input multiple-output (MIMO) systems are considered in this paper. We continue our research on uplink transmit beamforming design for multiple users under the assumption that the full multiuser channel state information, which is the collection of the channel state information between each of the users and the base station, is known not only to the receiver but also to all the transmitters. We propose an algorithm for designing optimal beamforming weights in terms of maximizing the signal-to-interference-plus-noise ratio (SINR). Through statistical modeling, we decouple the original mathematically intractable optimization problem and achieved a closed-form solution. As in our previous work, the minimum mean-squared error (MMSE) receiver with successive interference cancellation (SIC) is adopted for multiuser detection. The proposed scheme is compared with an existing jointly optimized transceiver design, referred to as the joint transceiver in this paper, and our previously proposed eigen-beamforming algorithm. Simulation results demonstrate that our algorithm, with much less computational burden, accomplishes almost the same performance as the joint transceiver for spatially independent MIMO channel and even better performance for spatially correlated MIMO channels. And it always works better than our previously proposed eigen beamforming algorithm.

In recent years, multi-input and multi-output (MIMO) radar systems have captured the attention of many researchers. At the very beginning, these works focused on increasing the signal-to-noise power ratio (SNR) by transmitting coherent signals. In 2004, a new concept called spatial diversity was introduced for MIMO radar, which achieved the same objective using widely separated transmitters and/or receivers and independent transmitted signals. This technology dramatically enhances the detection probability and accuracy in estimating direction of arrival (DOA) by efficiently constraining target scintillations and collecting more information carried in distinguishable signals. Moreover, since the transmitted signals are independent in MIMO radar, considerable mechanisms such as beamforming technologies and coherent processing, can be developed and applied to improve its performance. However, MIMO radar detection performance in a spatially correlated clutter environment does not get adequate attention that it deserves. Therefore, in this paper, received signals considered include reflections from the target, K-distributed clutter, and thermal noise. Moreover, beamforming technology and coherent processing are applied to estimate the reflectivity of and distinguish between target and clutter reflections. As a result, when echoes from target and clutter are unresolvable, the detection problem can be formulated as a two hypotheses test. According to the Bayesian approach, we develop the ratio test for this situation. In addition, we observe that if highly correlated information is utilized by adaptively modifying clutter local mean power probability density function (PDF), the uncertainty of clutter local mean power decreases and detection performance can be further improved. Last, we also compare the power based detection algorithm with the ratio test. Even though the power based detection algorithm has advantage in simple computation, its comparable performance is achieved only when number of transmitters is large.

Cooperative communication (CC) techniques, which form virtual multiple input multiple output (MIMO) systems through cooperation among users, have been prevailing in current academic research. Two scenarios that have been mostly considered are one source to one destination with help from a classic relay node and two sources to one destination with cooperation among sources, i.e. cooperation for multiple access channels. In either case, single antenna is employed at each node. In this paper, I propose to realize cooperation based on multiplexing for a broadcast channel where there is one source equipped with multiple antennas and two destinations with single antenna. One of the destinations experiencing better channels helps the other destination under worse channel conditions by serving as a relay. Such a channel is referred to as a multiple input single output (MISO) cooperative broadcast channel (CBC). Further, I consider the capacity analysis for the MISO CBC where additive white Gaussian noise (AWGN) presents (MISO AWGN CBC), which is not easy because MISO AWGN channel, as a vector Gaussian channel, is generally not degraded. I derive an outer bound on the capacity region of MISO AWGN CBC to provide insights into its information transmission limit.

A method is proposed for near-field narrowband source localization based on measurements from a uniform linear array (ULA) of sensors. This incoming field can be measured as a function of delay between each outer sensor and the center reference sensor. The approach uses a Fresnel approximation for the time delay between the sensor data as a function of direction of arrival and range. A minimum variance distortionless response (MVDR) filter is constructed to focus on any given near field location and the output is calculated when the recorded signal is passed through it. The intensity of this output for the various MVDR filters gives information on the source location and intensity.

Estimation of channel fading parameters is an important task in the design of communication links such as in maximum ratio combining (MRC), where the SNR of the link has to be estimated. The maximum combining weights are directly related to the SNR or the fading channel coefficients. In this paper, we propose iterative techniques based on Maximum Likelihood parameter estimation to estimate the parameters of Nakagami-m distribution in the presence of additive white Gaussian noise. We show that the proposed iterative algorithms converge to a unique solution independent of the initial condition. However, for the purpose of fast convergence, a method is used to find an initial condition close to the true solution. This initial condition is obtained by solving for the unique positive root of a polynomial. Comparisons of our proposed approaches are made with respect to the noise and initial conditions. The performance of the algorithm with respect to the Cramer-Rao bound (CRB) is investigated. Computer simulation results for different signal to noise ratios (SNR) are presented.

In this paper we estimate finite mixture models (FMM) to describe the statistics of the ultrawideband (UWB) channel amplitudes. Various combinations of Rayleigh, Nakagami, Weibull, and Lognormal distributions are used to form the constituent probability density functions (PDFs) of the FMMs. The FMMs are identified using the Stochastic Expectation Maximization (SEM) algorithm. Akaike's Information Criterion is used to compare the quality of data fit provided by the FMMs and models containing only one distribution (non-mixtures). The results indicate that UWB channel amplitude statistics are best represented by mixtures of Rayleigh, Lognormal and Weibull PDFs.