A probabilistic method for blind multiuser detection using array observations

In this paper, a blind algorithm for detecting active users in a DS-CDMA system is presented. This probabilistic algorithm relies on the theory of hidden Markov models (HMM) and is completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. Additionally, observation through an array of sensors is also considered. Performance is verified via computer simulations, showing the near-far resistance of the analyzed procedure.Peer ReviewedPostprint (published version

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for time-varying channel responses, a version of the algorithm, which is more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other Baum–Welch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed.Peer Reviewe

UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING

In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Probabilistic Algorithms for Blind Adaptive Multiuser Detection

In this paper, two probabilistic adaptive algorithms for jointly detecting active users in a DS-CDMA system are reported. The first one, which is based on the theory of hidden Markov models (HMM's) and the Baum--Wech (BW) algorithm, is proposed within the CDMA scenario and compared with the second one, which is a previously developed Viterbi-based algorithm. Both techniques are completely blind in the sense that no knowledge of the signatures, channel state information, or training sequences is required for any user. Once convergence has been achieved, an estimate of the signature of each user convolved with its physical channel response (CR) and estimated data sequences are provided. This CR estimate can be used to switch to any decision-directed (DD) adaptation scheme. Performance of the algorithms is verified via simulations as well as on experimental data obtained in an underwater acoustics (UWA) environment. In both cases, performance is found to be highly satisfactory, showing the near--far resistance of the analyzed algorithms

Probabilistic algorithms for blind adaptive multiuser detection

In this paper, two probabilistic adaptive algorithms for jointly detecting active users in a DS-CDMA system are reported. The first one, which is based on the theory of hidden Markov models (HMM’s) and the Baum–Wech (BW) algorithm, is proposed within the CDMA scenario and compared with the second one, which is a previously developed Viterbi-based algorithm. Both techniques are completely blind in the sense that no knowledge of the signatures, channel state information, or training sequences is required for any user. Once convergence has been achieved, an estimate of the signature of each user convolved with its physical channel response (CR) and estimated data sequences are provided. This CR estimate can be used to switch to any decision-directed (DD) adaptation scheme. Performance of the algorithms is verified via simulations as well as on experimental data obtained in an underwater acoustics (UWA) environment. In both cases, performance is found to be highly satisfactory, showing the near–far resistance of the analyzed algorithms.Peer Reviewe