977 research outputs found
Semi-blind sparse channel estimation with constant modulus symbols
We propose two methods for the estimation of sparse communication channels. In the first method, we consider the problem of channel estimation based on training symbols, and formulate it as an optimization problem. In this formulation, we combine the objective of fidelity to the received data with a non-quadratic constraint reflecting the prior information about the sparsity of the channel. This approach leads to accurate channel estimates with much shorter training sequences than conventional methods. The second method we propose is aimed at taking advantage of any available training-based data, as well as any "blind" data based on unknown, constant modulus symbols. We propose a semi-blind optimization framework making use of these two types of data, and enforcing the sparsity of the channel, as well as the constant modulus property of the symbols. This approach improves upon the channel estimates based only on training sequences, and also produces accurate estimates for the unknown symbols
Channel Protection: Random Coding Meets Sparse Channels
Multipath interference is an ubiquitous phenomenon in modern communication
systems. The conventional way to compensate for this effect is to equalize the
channel by estimating its impulse response by transmitting a set of training
symbols. The primary drawback to this type of approach is that it can be
unreliable if the channel is changing rapidly. In this paper, we show that
randomly encoding the signal can protect it against channel uncertainty when
the channel is sparse. Before transmission, the signal is mapped into a
slightly longer codeword using a random matrix. From the received signal, we
are able to simultaneously estimate the channel and recover the transmitted
signal. We discuss two schemes for the recovery. Both of them exploit the
sparsity of the underlying channel. We show that if the channel impulse
response is sufficiently sparse, the transmitted signal can be recovered
reliably.Comment: To appear in the proceedings of the 2009 IEEE Information Theory
Workshop (Taormina
OFDM demodulation in underwater time-reversed shortned channels
This work addresses the problem of OFDM transmission
in dispersive underwater channels where impulse responses
lasting tens of miliseconds cannot be reliably handled
by recently proposed methods due to limitations of channel
estimation algorithms. The proposed approach relies on passive
time reversal for multichannel combining of observed waveforms
at an array of sensors prior to OFDM processing, which produces
an equivalent channel with a shorter impulse response that
can be handled much more easily. A method for tracking
the narrowband residual phase variations of the channel after
Doppler preprocessing is proposed. This is a variation of an
existing technique that can improve the spectral efficiency of
OFDM by reducing the need for pilot symbols. This work also
examines techniques to handle sparse impulse responses and
proposes a channel estimation method where an l1 norm is
added to the standard least-squares cost function to transparently
induce sparseness in the vector of channel coefficients. Algorithms
are assessed using data collected during the UAB’07 experiment,
which was conducted in Trondheim fjord, Norway, in September
2007. Data were transmitted with bandwidths of 1.5 and 4.5 kHz,
and recorded at a range of about 800 m in a 16-hydrophone array.
Significant multipath was observed over a period of at least 30
ms.FC
Signal Processing Design of Low Probability of Intercept Waveforms
This thesis investigates a modification to Differential Phase Shift Keyed (DPSK) modulation to create a Low Probability of Interception/Exploitation (LPI/LPE) communications signal. A pseudorandom timing offset is applied to each symbol in the communications stream to intentionally create intersymbol interference (ISI) that hinders accurate symbol estimation and bit sequence recovery by a non-cooperative receiver. Two cooperative receiver strategies are proposed to mitigate the ISI due to symbol timing offset: a modified minimum Mean Square Error (MMSE) equalization algorithm and a multiplexed bank of equalizer filters determined by an adaptive Least Mean Square (LMS) algorithm. Both cooperative receivers require some knowledge of the pseudorandom symbol timing dither to successfully demodulate the communications waveform. Numerical Matlab® simulation is used to demonstrate the bit error rate performance of cooperative receivers and notional non-cooperative receivers for binary, 4-ary, and 8-ary DPSK waveforms transmitted through a line-of-sight, additive white Gaussian noise channel. Simulation results suggest that proper selection of pulse shape and probability distribution of symbol timing offsets produces a waveform that is accurately demodulated by the proposed cooperative receivers and significantly degrades non-cooperative receiver symbol estimation accuracy. In typical simulations, non-cooperative receivers required 2-8 dB more signal power than cooperative receivers to achieve a bit error rate of 1.0%. For nearly all reasonable parameter selections, non-cooperative receivers produced bit error rates in excess of 0.1%, even when signal power is unconstrained
Blind Signal Separation for Digital Communication Data
to appear in EURASIP E-reference in Signal Processing, invited paper.International audienceBlind source separation, often called independent component analysis , is a main field of research in signal processing since the eightees. It consists in retrieving the components, up to certain indeterminacies, of a mixture involving statistically independent signals. Solid theoretical results are known; besides, they have given rise to performent algorithms. There are numerous applications of blind source separation. In this contribution, we particularize the separation of telecommunication sources. In this context, the sources stem from telecommunication devices transmitting at the same time in a given band of frequencies. The received data is a mixed version of all these sources. The aim of the receiver is to isolate (separate) the different contributions prior to estimating the unknown parameters associated with a transmitter. The context of telecommunication signals has the particularity that the sources are not stationary but cyclo-stationary. Now, in general, the standard methods of blind source separation assume the stationarity of the sources. In this contribution , we hence make a survey of the well-known methods and show how the results extend to cyclo-stationary sources
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