Downlink Video Streaming for Users Non-Equidistant from Base Station

We consider multiuser video transmission for users that are non-equidistantly positioned from base station. We propose a greedy algorithm for video streaming in a wireless system with capacity achieving channel coding, that implements the cross-layer principle by partially separating the physical and the application layer. In such a system the parameters at the physical layer are dependent on the packet length and the conditions in the wireless channel and the parameters at the application layer are dependent on the reduction of the expected distortion assuming no packet errors in the system. We also address the fairness in the multiuser video system with non-equidistantly positioned users. Our fairness algorithm is based on modified opportunistic round robin scheduling. We evaluate the performance of the proposed algorithms by simulating the transmission of H.264/AVC video signals in a TDMA wireless system

Sparse Covariance Fitting Method for Direction of Arrival Estimation of Uncorrelated Wideband Signals

We propose a new direction of arrival estimation method for wideband uncorrelated signals. The wideband signals are first decomposed into narrowband signals. A group sparse Lasso formulation is proposed that jointly fits the powers of the signals using overcomplete dictionaries of the directions of arrival, into the estimated covariance matrices for all narrowband signals. Then, we propose a new formulation that determines the regularization parameters and becomes the group Lasso formulation. Additionally, we propose a modified algorithm for direction of arrival estimation with lower complexity that uses conventional based methods in the preprocessing stage to reduce the number of variables in the optimization task. We compare the performance of the proposed method to the conventional methods for a circular antenna array

Improved Asymptotic Capacity Lower Bound for OFDM System with Compressed Sensing Channel Estimation for Bernoulli Gaussian Channel

We propose an improved capacity lower bound for OFDM system with compressed sensing channel estimation for Bernoulli-Gaussian channel. We improve the known capacity lower bound which is based on Lasso compressed sensing channel estimation, by replacing the Lasso based estimate with an MMSE estimate, known to be optimal in the MMSE sense and achievable with practical algorithms for a broad range of system parameters setup. Additionally, for the system with equi-powered pilot subcarriers we optimize the capacity lower bound by finding the optimal average fraction of pilot subcarriers used for channel estimation and optimal pilot to data power ratio given the average symbol power per subcarrier, and propose an optimization procedure with polynomial complexity