Zero-delay source-channel coding

In this thesis, we investigate the zero-delay transmission of source samples over three different types of communication channel models. First, we consider the zero-delay transmission of a Gaussian source sample over an additive white Gaussian noise (AWGN) channel in the presence of an additive white Gaussian (AWG) interference, which is fully known by the transmitter. We propose three parameterized linear and non-linear transmission schemes for this scenario, and compare the corresponding mean square error (MSE) performances with that of a numerically optimized encoder, obtained using the necessary optimality conditions. Next, we consider the zero-delay transmission of a Gaussian source sample over an AWGN channel with a one-bit analog-to-digital (ADC) front end. We study this problem under two different performance criteria, namely the MSE distortion and the distortion outage probability (DOP), and obtain the optimal encoder and the decoder for both criteria. As generalizations of this scenario, we consider the performance with a K-level ADC front end as well as with multiple one-bit ADC front ends. We derive necessary conditions for the optimal encoder and decoder, which are then used to obtain numerically optimized encoder and decoder mappings. Finally, we consider the transmission of a Gaussian source sample over an AWGN channel with a one-bit ADC front end in the presence of correlated side information at the receiver. Again, we derive the necessary optimality conditions, and using these conditions obtain numerically optimized encoder and decoder mappings. We also consider the scenario in which the side information is available also at the encoder, and obtain the optimal encoder and decoder mappings. The performance of the latter scenario serves as a lower bound on the performance of the case in which the side information is available only at the decoder.Open Acces

Wireless information and power transfer over an AWGN channel: Nonlinearity and asymmetric Gaussian signaling

Simultaneous transmission of information and power over a point-to-point flat-fading complex Additive White Gaussian Noise (AWGN) channel is studied. In contrast with the literature that relies on an inaccurate linear model of the energy harvester, an experimentally-validated nonlinear model is considered. A general form of the delivered Direct Current (DC) power in terms of system baseband parameters is derived, which demonstrates the dependency of the delivered DC power on higher order statistics of the channel input distribution. The optimization problem of maximizing Rate-Power (R-P) region is studied. Assuming that the Channel gain is available at both the receiver and the transmitter, and constraining to independent and identically distributed (i.i.d.) channel inputs determined only by their first and second moment statistics, an inner bound for the general problem is obtained. Notably, as a consequence of the harvester nonlinearity, the studied inner bound exhibits a tradeoff between the delivered power and the rate of received information. It is shown that the tradeoff-characterizing input distribution is with mean zero and with asymmetric power allocations to the real and imaginary dimensions

Zero-Delay Source-Channel Coding With a Low-Resolution ADC Front End

Motivated by the practical constraints arising in emerging sensor network and Internet-of-Things (IoT) applications, the zero-delay transmission of a Gaussian measurement over a real single-input multiple-output (SIMO) additive white Gaussian noise (AWGN) channel is studied with a low-resolution analog-to-digital converter (ADC) front end. Joint optimization of the encoder and the decoder mapping is tackled under both the mean squared error (MSE) distortion and the distortion outage probability (DOP) criteria, with an average power constraint on the channel input. Optimal encoder and decoder mappings are identified for a one-bit ADC front end under both criteria. For the MSE distortion, the optimal encoder mapping is shown to be non-linear in general, while it tends to a linear encoder in the low signal-to-noise ratio (SNR) regime, and to an antipodal digital encoder in the high SNR regime. This is in contrast to the optimality of linear encoding at all SNR values in the presence of a full-precision front end. For the DOP criterion, it is shown that the optimal encoder mapping is piecewise constant and can take only two opposite values when it is non-zero. For both the MSE distortion and the DOP criteria, necessary optimality conditions are then derived for $K$ -level ADC front ends as well as front ends with multiple one-bit ADCs. These conditions are used to obtain numerically optimized solutions. Extensive numerical results are also provided in order to gain insights into the structure of the optimal encoding and decoding mappings

SWIPT Signalling over Complex AWGN Channels with Two Nonlinear Energy Harvester Models

Simultaneous Wireless Information and Power Transfer (SWIPT) is subject to nonlinearity at the energy harvester that leads to significant changes to transmit signal designs compared to conventional wireless communications. In this paper, the capacity of a discrete time, memoryless and complex Additive White Gaussian Noise (AWGN) channel in the presence of a nonlinear energy harvester at the receiver is studied. Considering the two common nonlinear energy harvester models introduced in the literature, two sets of constraints are considered. First the capacity is studied under average power (AP), peak amplitude (PA) and receiver delivery power (RDP) constraints. The RDP constraint is modelled as a linear combination of even-moment statistics of the channel input being larger than a threshold. It is shown that the capacity of an AWGN channel under AP and RDP constraints is the same as the capacity of an AWGN channel under an AP constraint, however, depending on the two constraints, it can be either achieved or arbitrarily approached. It is also shown that under AP, PA and RDP constraints, the amplitude of the optimal inputs is discrete with a finite number of mass points. Next, the capacity is studied under AP, PA and output outage probability (OOP) constraints. OOP is modelled as satisfying a certain probability inequality for the amplitude of the received signal being outside of a given interval. Similarly, it is shown that the amplitude of the optimal input is discrete with a finite number of mass points