This paper investigates delay-distortion-power trade offs in transmission of quasi-stationary sources over block fading channels by studying encoder and decoder buffering techniques to smooth out the source and channel variations. Four source and cha
nnel coding schemes that consider buffer and power constraints are presented to minimize the reconstructed source distortion. The first one is a high performance scheme, which benefits from optimized source and channel rate adaptation. In the second scheme, the channel coding rate is fixed and optimized along with transmission power with respect to channel and source variations; hence this scheme enjoys simplicity of implementation. The two last schemes have fixed transmission power with optimized adaptive or fixed channel coding rate. For all the proposed schemes, closed form solutions for mean distortion, optimized rate and power are provided and in the high SNR regime, the mean distortion exponent and the asymptotic mean power gains are derived. The proposed schemes with buffering exploit the diversity due to source and channel variations. Specifically, when the buffer size is limited, fixed channel rate adaptive power scheme outperforms an adaptive rate fixed power scheme. Furthermore, analytical and numerical results demonstrate that with limited buffer size, the system performance in terms of reconstructed signal SNR saturates as transmission power is increased, suggesting that appropriate buffer size selection is important to achieve a desired reconstruction quality.
In this paper, a general algorithm is proposed for rate analysis and code design of linear index coding problems. Specifically a solution for minimum rank matrix completion problem over finite fields representing the linear index coding problem is de
vised in order to find the optimum transmission rate given vector length and size of the field. The new approach can be applied to both scalar and vector linear index coding.
This paper investigates the capacity and capacity per unit cost of Gaussian multiple access-channel (GMAC) with peak power constraints. We first devise an approach based on Blahut-Arimoto Algorithm to numerically optimize the sum rate and quantify th
e corresponding input distributions. The results reveal that in the case with identical peak power constraints, the user with higher SNR is to have a symmetric antipodal input distribution for all values of noise variance. Next, we analytically derive and characterize an achievable rate region for the capacity in cases with small peak power constraints, which coincides with the capacity in a certain scenario. The capacity per unit cost is of interest in low power regimes and is a target performance measure in energy efficient communications. In this work, we derive the capacity per unit cost of additive white Gaussian channel and GMAC with peak power constraints. The results in case of GMAC demonstrate that the capacity per unit cost is obtained using antipodal signaling for both users and is independent of users rate ratio. We characterize the optimized transmission strategies obtained for capacity and capacity per unit cost with peak-power constraint in detail and specifically in contrast to the settings with average-power constraints.
We are interested in understanding the neural correlates of attentional processes using first principles. Here we apply a recently developed first principles approach that uses transmitted information in bits per joule to quantify the energy efficien
cy of information transmission for an inter-spike-interval (ISI) code that can be modulated by means of the synchrony in the presynaptic population. We simulate a single compartment conductance-based model neuron driven by excitatory and inhibitory spikes from a presynaptic population, where the rate and synchrony in the presynaptic excitatory population may vary independently from the average rate. We find that for a fixed input rate, the ISI distribution of the post synaptic neuron depends on the level of synchrony and is well-described by a Gamma distribution for synchrony levels less than 50%. For levels of synchrony between 15% and 50% (restricted for technical reasons), we compute the optimum input distribution that maximizes the mutual information per unit energy. This optimum distribution shows that an increased level of synchrony, as it has been reported experimentally in attention-demanding conditions, reduces the mode of the input distribution and the excitability threshold of post synaptic neuron. This facilitates a more energy efficient neuronal communication.
Studying the development of malignant tumours, it is important to know and predict the proportions of different cell types in tissue samples. Knowing the expected temporal evolution of the proportion of normal tissue cells, compared to stem-like and
non-stem like cancer cells, gives an indication about the progression of the disease and indicates the expected response to interventions with drugs. Such processes have been modeled using Markov processes. An essential step for the simulation of such models is then the determination of state transition probabilities. We here consider the experimentally more realistic scenario in which the measurement of cell population sizes is noisy, leading to a particular hidden Markov model. In this context, randomness in measurement is related to noisy measurements, which are used for the estimation of the transition probability matrix. Randomness in sampling, on the other hand, is here related to the error in estimating the state probability from small cell populations. Using aggregated data of fluorescence-activated cell sorting (FACS) measurement, we develop a minimum mean square error estimator (MMSE) and maximum likelihood (ML) estimator and formulate two problems to find the minimum number of required samples and measurements to guarantee the accuracy of predicted population sizes using a transition probability matrix estimated from noisy data. We analyze the properties of two estimators for different noise distributions and prove an optimal solution for Gaussian distributions with the MMSE. Our numerical results show, that for noisy measurements the convergence mechanism of transition probabilities and steady states differ widely from the real values if one uses the standard deterministic approach in which measurements are assumed to be noise free.
