Claudio R. C. M. da Silva

Asynchronous Classification of Digital Amplitude-Phase Modulated Signals in Flat-Fading Channels

This paper presents a new asynchronous modulation classifier for digital amplitude-phase modulated signals in flat-fading channels when the channel state is assumed unknown. In the design of this classifier, we propose new estimators for the unknown amplitude, time offset, and noise power that are blind to the modulation scheme of the received signal. It is shown that the proposed classifier performs well compared to the optimal classifier with perfect channel knowledge for an adequate estimation interval.

Partially Connected Interference Networks with No CSIT: Symmetric Degrees of Freedom and Multicast Across Alignment Blocks

We explore the symmetric degrees of freedom (DoF) of partially connected interference networks for the case in which transmitters have no knowledge of their channel states (channel coefficients and coherence times) but are aware of the network topology. In particular, we consider interference networks which have no internal conflicts in their alignment graph. This class of networks can be used to model various interference scenarios in modern wireless systems. A sufficient condition is established for which half DoF can be achieved per message, which coincide with the best known outer bound, thereby establishing the optimal symmetric DoF. For networks in this class that do not satisfy the sufficient condition, a systematic achievable scheme, termed multicast across alignment blocks, is derived and an analytical expression is obtained for the achieved symmetric DoF. The new scheme is based on our insight that an interference alignment-based scheme may not utilize all available signalling dimensions, and the unused signalling dimensions can be further exploited through multicast transmission.

Cumulant-based channel estimation algorithm for modulation classification in frequency-selective fading channels

Due to the lack of sufficient channel state information, modulation classification in frequency-selective fading channels is a challenging task. This is mainly because the complexity of the pre-processing stage, where the required channel state information for (optimal) likelihood-based classification is estimated, can be relatively high. For this reason, we propose in this paper a low-complexity cumulant-based channel estimation algorithm that enables the reliable classification of digital amplitude-phase modulated signals in frequency-selective fading channels. Numerical results are presented which show that the proposed algorithm outperforms a commonly used channel estimation algorithm for modulation classification, especially at low signal-to-noise ratio values. Using the proposed algorithm, it is also shown that the performance of a practical modulation classification method can approach that of a clairvoyant classifier assumed to have perfect channel knowledge.

A Blind Preprocessor for Modulation Classification Applications in Frequency-Selective Non-Gaussian Channels

This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood- or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.

Blind Signal Detection Using a Linear Antenna Array: An Experimental Approach

An experimental investigation is reported on the use of a linear antenna array to perform blind signal detection. A software-defined-radio (SDR)-based receiver system is used to capture signals received by an antenna array while ensuring time and frequency synchronization across the radio-frequency (RF) front ends. The antenna array is moved in circular and linear paths to cause variation in received signal strength and to cause variation in the incident angles of the incoming waves. Covariance-based detection (CBD) algorithms are applied to the received signals to perform blind signal detection. Multiple antennas serve to enhance the received signal correlation, thereby causing an improvement in the detection performance of the CBD algorithms. In the presence of noise calibration error (which is inevitable in a practical system), the maximum eigenvalue of the correlation matrix (MEC), Hadamard ratio test (HRT), and covariance absolute value (CAV) algorithms exhibit a significant improvement in detection performance as the number of antennas is increased, whereas the eigenvalue-based detection (EBD) algorithms show no improvement. This paper also investigates the effect of varying the antenna spacing on the received signal correlation and its subsequent effect on the detection performance. It is observed that the detection performance of the linear array is directly related to the mean signal cross-correlation achieved by the array. A significant improvement in detection performance was observed for an antenna spacing of 0.1λ; however, to avoid degradation of antenna efficiency at such close spacing, proper impedance matching must be performed.

Relay feedback-based power control in multihop wireless networks

In this paper, we propose a distributed power control scheme which can take into account the end-to-end performance of users in a multihop cellular network. Bottleneck links in a multihop wireless network limit the maximum rate achieved by users along their routes. Under the proposed scheme a link dynamically switches between different power adaptation algorithms based on whether it is a bottleneck or a non-bottleneck link. Specifically, non-bottleneck links iteratively reduce their transmit powers whereas the bottleneck links use power control to exploit the corresponding interference reduction and further increase their signal-to-noise-and-interference ratios (SINRs). We show substantial system throughput performance gain under our scheme and for which, under certain scenarios, we derive a closed-form expression of the converged transmit power levels regardless of their initial values.

Maximum-likelihood modulation classification with incomplete channel information

This paper presents a discussion of the classification of digital communication signals given incomplete knowledge of the channel. Through a maximum-likelihood framework, modulation classifiers are presented which assume no or limited a priori knowledge of the fading experienced by the signal (including time offset, phase shift, and amplitude) and/or the distribution of the noise added in the channel. A recently published asynchronous classifier for digitally modulated signals, which uses a new channel estimator that is blind to the modulation scheme of the received signal, is introduced and analyzed. In addition, results are presented of our recent work on the classification of digitally modulated signals in flat fading non-Gaussian channels.

Performance Analysis of a Multiband OFDM UWB System in the Presence of Narrowband Interference

This paper presents a performance analysis of an ultra wideband (UWB) communication system based on Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) in the presence of narrowband interference. Although OFDM-based systems are known to be capable of withstanding some level of narrowband interference, mitigation techniques may have to be used when interference levels are high. Furthermore, as typical UWB systems employ low resolution analog-to-digital converters, the effect of narrowband interference may be exacerbated in the process of analog-to-digital conversion. In order to improve the performance of MB-OFDM UWB systems in the presence of narrowband interference, we consider the use of analog notch filters. This paper proposes an analytical model and formulation that allows for the evaluation of the probability of symbol error of an MB-OFDM UWB system when an analog notch filter is used for narrowband interference mitigation. It is shown that using an analog notch filter can significantly improve the performance of MB-OFDM UWB systems in high-interference scenarios.

Feasibility Study of Outdoor Wireless Communication in the 60 GHz Band

In 2001, the Federal Communications Commission made available a large block of spectrum known as the 60 GHz band. The 60 GHz band is attractive because it provides the opportunity of multi-Gbps data rates with unlicensed commercial use. One of the main challenges facing the use of this band is poor propagation characteristics including high path loss and strong attenuation due to oxygen absorption. Antenna arrays have been proposed as a means of combating these effects. In this paper we study the feasibility of outdoor communication in the 60 GHz band. Because arrays are required for antenna gain and adaptability, we explore the use of arrays as a form of equalization in the presence of channel-induced intersymbol interference. A site-specific study is conducted using ray tracing to model an outdoor environment on the Virginia Tech campus. The performance of outdoor links is evaluated through simulation of the bit error probability.

An Optimization Approach to Single-Source Localization Using Direction and Range Estimates

This paper presents novel single-source localization techniques for a sensor network that is capable of obtaining both direction and range estimates by considering an optimization approach. Specifically, linear programming and non-linear programming techniques are developed and compared to a classic linear least squares technique. It is shown that the proposed techniques achieve superior results to the least squares approach in most cases.