33,029 research outputs found
Syndrome-Based Encoding of Compressible Sources for M2M Communication
Data originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre- quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli- uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable
Oversampling PCM techniques and optimum noise shapers for quantizing a class of nonbandlimited signals
We consider the efficient quantization of a class of nonbandlimited signals, namely, the class of discrete-time signals that can be recovered from their decimated version. The signals are modeled as the output of a single FIR interpolation filter (single band model) or, more generally, as the sum of the outputs of L FIR interpolation filters (multiband model). These nonbandlimited signals are oversampled, and it is therefore reasonable to expect that we can reap the same benefits of well-known efficient A/D techniques that apply only to bandlimited signals. We first show that we can obtain a great reduction in the quantization noise variance due to the oversampled nature of the signals. We can achieve a substantial decrease in bit rate by appropriately decimating the signals and then quantizing them. To further increase the effective quantizer resolution, noise shaping is introduced by optimizing prefilters and postfilters around the quantizer. We start with a scalar time-invariant quantizer and study two important cases of linear time invariant (LTI) filters, namely, the case where the postfilter is the inverse of the prefilter and the more general case where the postfilter is independent from the prefilter. Closed form expressions for the optimum filters and average minimum mean square error are derived in each case for both the single band and multiband models. The class of noise shaping filters and quantizers is then enlarged to include linear periodically time varying (LPTV)M filters and periodically time-varying quantizers of period M. We study two special cases in great detail
Optimum quantization of a class of non-bandlimited signals
We consider the quantization of a special class of non
bandlimited signals, namely the class of discrete time signals that can be recovered from their decimated version. Similar to sigma-delta modulation ideas, we show that we can obtain a great reduction in the quantization noise variance due to the oversampled nature of these signals. We then consider noise shaping by optimizing a pre- and post filter around the quantizer and develop a closed form expression for the coding gain of the scheme under study. The use of
an orthonormal filter bank as a sophisticated quantizer is
investigated and several examples are provided
Antenna Combining for the MIMO Downlink Channel
A multiple antenna downlink channel where limited channel feedback is
available to the transmitter is considered. In a vector downlink channel
(single antenna at each receiver), the transmit antenna array can be used to
transmit separate data streams to multiple receivers only if the transmitter
has very accurate channel knowledge, i.e., if there is high-rate channel
feedback from each receiver. In this work it is shown that channel feedback
requirements can be significantly reduced if each receiver has a small number
of antennas and appropriately combines its antenna outputs. A combining method
that minimizes channel quantization error at each receiver, and thereby
minimizes multi-user interference, is proposed and analyzed. This technique is
shown to outperform traditional techniques such as maximum-ratio combining
because minimization of interference power is more critical than maximization
of signal power in the multiple antenna downlink. Analysis is provided to
quantify the feedback savings, and the technique is seen to work well with user
selection and is also robust to receiver estimation error.Comment: Submitted to IEEE Trans. Wireless Communications April 2007. Revised
August 200
Real-valued average consensus over noisy quantized channels
This paper concerns the average consensus problem
with the constraint of quantized communication between
nodes. A broad class of algorithms is analyzed, in which the
transmission strategy, which decides what value to communicate
to the neighbours, can include various kinds of rounding, probabilistic
quantization, and bounded noise. The arbitrariness
of the transmission strategy is compensated by a feedback
mechanism which can be interpreted as a self-inhibitory action.
The result is that the average of the nodes state is not conserved
across iterations, and the nodes do not converge to a consensus;
however, we show that both errors can be made as small
as desired. Bounds on these quantities involve the spectral
properties of the graph and can be proved by employing
elementary techniques of LTI systems analysis
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