23,342 research outputs found
Blind multiuser detection using hidden markov models theory
We present an adaptive algorithm based on the theory of hidden Markov models (HMM) which is capable of jointly detecting the users in a DS-CDMA system. The proposed technique is near-far resistant and completely blind in the sense that no knowledge of the signature sequences, channel state information or training sequences is required for any user. In addition to this, an estimate of the signature of each user convolved with its physical channel impulse response (CIR), and an estimate of the background noise variance are provided once convergence is achieved (as well as estimated data sequences). At this moment, and using that CIR estimate, we can switch to any decision-directed (DD) adaptation scheme.Peer ReviewedPostprint (published version
Application of hidden markov models to blind channel estimation and data detection in a gsm environment
In this paper, we present an algorithm based on the Hidden Markov Models (HMM) theory to solve the problem of blind channel estimation and sequence detection in mobile digital communications. The environment in which the algorithm is tested is the Paneuropean Mobile Radio System, also known as GSM. In this system, a large part in each burst is devoted to allocate a training sequence used to obtain a channel estimate. The algorithm presented would not require this sequence, and that would imply an increase of the system capacity. Performance, evaluated for standard test channels, is close to that of non-blind algorithms.Peer ReviewedPostprint (published version
Analysis of Second-order Statistics Based Semi-blind Channel Estimation in CDMA Channels
The performance of second order statistics (SOS) based semi-blind channel
estimation in long-code DS-CDMA systems is analyzed. The covariance matrix of
SOS estimates is obtained in the large system limit, and is used to analyze the
large-sample performance of two SOS based semi-blind channel estimation
algorithms. A notion of blind estimation efficiency is also defined and is
examined via simulation results.Comment: To be presented at the 2005 Conference on Information Sciences and
System
Wavelet Based Semi-blind Channel Estimation For Multiband OFDM
This paper introduces an expectation-maximization (EM) algorithm within a
wavelet domain Bayesian framework for semi-blind channel estimation of
multiband OFDM based UWB communications. A prior distribution is chosen for the
wavelet coefficients of the unknown channel impulse response in order to model
a sparseness property of the wavelet representation. This prior yields, in
maximum a posteriori estimation, a thresholding rule within the EM algorithm.
We particularly focus on reducing the number of estimated parameters by
iteratively discarding ``unsignificant'' wavelet coefficients from the
estimation process. Simulation results using UWB channels issued from both
models and measurements show that under sparsity conditions, the proposed
algorithm outperforms pilot based channel estimation in terms of mean square
error and bit error rate and enhances the estimation accuracy with less
computational complexity than traditional semi-blind methods
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
ALOHA With Collision Resolution(ALOHA-CR): Theory and Software Defined Radio Implementation
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. If more
than two users transmit in the same slot the collision cannot be resolved and
retransmission is required. If only one user transmits, the transmitted packet
is recovered with some probability, depending on the state of the channel. If
two users transmit the collision is resolved and the packets are recovered by
first over-sampling the collision signal and then exploiting independent
information about the two users that is contained in the signal polyphase
components. The ALOHA-CR throughput is derived under the infinite backlog
assumption and also under the assumption of finite backlog. The contention
probability is determined under these two assumptions in order to maximize the
network throughput and maintain stability. Queuing delay analysis for network
users is also conducted. The performance of ALOHA-CR is demonstrated on the
Wireless Open Access Research Platform (WARP) test-bed containing five software
defined radio nodes. Analysis and test-bed results indicate that ALOHA-CR leads
to significant increase in throughput and reduction of service delays
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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