Niklas Wernersson

Distributed quantization over noisy channels

The problem of designing simple and energy-efficient sensor nodes in a wireless sensor network is considered from a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers for orthogonal channels is proposed and evaluated. In particular the cases of the binary symmetric channel as well as the additive white Gaussian noise channel are studied. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non- ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.

Nonlinear coding and estimation for correlated data in wireless sensor networks

The problem of designing simple and energy efficient nonlinear distributed source-channel codes is considered. By demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. Based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. One of the main advantages of the proposed scheme is that it is implementable for many sources, contrary to most existing nonlinear distributed source-channel coding systems.

Polynomial based analog source-channel codes

In many communication applications one is interested in transmitting a time-discrete analog-valued (i.e. continuous alphabet) source over a time-discrete analog channel. We study this problem in the case of bandwidth expansion, in the sense that one source sample, X, is transmitted over N orthogonal channels. An analog source-channel code based on orthogonal polynomials is proposed and analyzed. The code can be generated using a Gram-Schmidt procedure, to fit virtually any source distribution.

Zero-Delay Joint Source-Channel Coding for a Bivariate Gaussian on a Gaussian MAC

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

Polynomial based analog source-channel codes - [transactions papers]

In many communication applications one is interested in transmitting a time-discrete analog-valued (i.e. continuous alphabet) source over a time-discrete analog channel. We study this problem in the case of bandwidth expansion, in the sense that one source sample, X, is transmitted over N-orthogonal channels. An analog source-channel code based on orthogonal polynomials is proposed and analyzed. The code can be generated using a Gram-Schmidt procedure, to fit virtually any source distribution.

Analog Source-Channel Codes Based on Orthogonal Polynomials

In many communication applications one is interested in transmitting a time-discrete analog-valued (i.e. continues alphabet) source over a time-discrete analog channel. We study this problem in the case of bandwidth expansion, in the sense that one source sample, X, is transmitted over N orthogonal channels. An analog source-channel code based on orthogonal polynomials is proposed and analyzed. The code can be generated using a Gram-Schmidt procedure, to fit virtually any source distribution.

Distributed Scalar Quantizers for Noisy Channels

Sensor nodes in wireless sensor networks should preferable be both cheap and energy efficient. To cope with these requirements an algorithm for designing distributed scalar quantizers optimized for noisy channels is proposed and evaluated. Applying the algorithm results in locally optimal systems. It is demonstrated that the correlation between the sources can be used to reduce the quantization distortion when the channel is close to error-free. If, on the other hand, there are a lot of disturbances on the channel the correlation can be used to introduce protection against channel errors.

Nonlinear distributed sensing for closed-loop control over Gaussian channels

A scenario of distributed sensing for networked control systems is considered and a new approach to distributed sensing and transmission is presented. The state process of a scalar first order linear time invariant dynamical system is sensed by a network of wireless sensors, which then instantaneously transmit their measurements to a remotely situated control unit over parallel Gaussian channels. The control unit aims to stabilize the system in mean square sense. The proposed non-linear delay-free sensing and transmission strategy is compared with the well-known amplify-and-forward strategy, using the LQG control cost as a figure of merit. It is demonstrated that the proposed nonlinear scheme outperforms the best linear scheme even when there are only two sensors in the network. The proposed sensing and transmission scheme can be implemented with a reasonable complexity and it is shown to be robust to the uncertainties in the knowledge of the sensors about the statistics of the measurement noise and the channel noise.

On source decoding based on finite-bandwidth soft information

Designing a communication system using joint source-channel coding in general makes it possible to achieve a better performance than when the source and channel codes are designed separately, especially under strict delay-constraints. The majority of work done in joint source-channel coding uses a discrete channel model, corresponding to an analog channel in conjunction with a hard decision modulation scheme. The performance of such a system can however be improved by using soft decision modulation. The main cost is a higher decoding complexity. An alternative is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this paper we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. We achieve a performance close to that of a system using unquantized soft information

Distributed Scalar Quantizers for Gaussian Channels

The problem of designing simple and energy efficient sensor nodes for a wireless sensor network is considered in a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers optimized for orthogonal additive white Gaussian noise channels is proposed and evaluated. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non-ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.