@inproceedings{Koch2010,
title = {Increased Capacity per Unit-Cost by Oversampling},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5662127},
isbn = {978-1-4244-8681-6},
year = {2010},
date = {2010-01-01},
booktitle = {2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel},
pages = {000684--000688},
publisher = {IEEE},
address = {Eliat},
abstract = {It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.},
keywords = {AWGN, AWGN channels, bandlimited Gaussian channel, channel capacity, Gaussian channels, increased capacity per unit cost, Information rates, one bit output quantizer, oversampling, quantisation (signal), quantization, sampling rate recovery, signal sampling},
pubstate = {published},
tppubtype = {inproceedings}
}

It is demonstrated that doubling the sampling rate recovers some of the loss in capacity incurred on the bandlimited Gaussian channel with a one-bit output quantizer.

@inproceedings{Koch2008b,
title = {Multipath Channels of Unbounded Capacity},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=4736611},
isbn = {978-1-4244-2481-8},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel},
pages = {640--644},
publisher = {IEEE},
address = {Eilat},
abstract = {The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.},
keywords = {channel capacity, discrete-time capacity, Entropy, Fading, fading channels, Frequency, H infinity control, Information rates, multipath channels, multipath fading channels, noncoherent, noncoherent capacity, path gains decay, Signal to noise ratio, statistics, Transmitters, unbounded capacity},
pubstate = {published},
tppubtype = {inproceedings}
}

The capacity of discrete-time, noncoherent, multipath fading channels is considered. It is shown that if the variances of the path gains decay faster than exponentially, then capacity is unbounded in the transmit power.

@inproceedings{Rodrigues2008,
title = {Multiple-Input Multiple-Output Gaussian Channels: Optimal Covariance for Non-Gaussian Inputs},
author = {Miguel R D Rodrigues and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4578704},
isbn = {978-1-4244-2269-2},
year = {2008},
date = {2008-01-01},
booktitle = {2008 IEEE Information Theory Workshop},
pages = {445--449},
publisher = {IEEE},
address = {Porto},
abstract = {We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-point equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.},
keywords = {Binary phase shift keying, covariance matrices, Covariance matrix, deterministic MIMO Gaussian channel, fixed-point equation, Gaussian channels, Gaussian noise, Information rates, intersymbol interference, least mean squares methods, Magnetic recording, mercury-waterfilling power allocation policy, MIMO, MIMO communication, minimum mean-squared error, MMSE, MMSE matrix, multiple-input multiple-output system, Multiple-Input Multiple-Output Systems, Mutual information, Optimal Input Covariance, Optimization, Telecommunications},
pubstate = {published},
tppubtype = {inproceedings}
}

We investigate the input covariance that maximizes the mutual information of deterministic multiple-input multipleo-utput (MIMO) Gaussian channels with arbitrary (not necessarily Gaussian) input distributions, by capitalizing on the relationship between the gradient of the mutual information and the minimum mean-squared error (MMSE) matrix. We show that the optimal input covariance satisfies a simple fixed-point equation involving key system quantities, including the MMSE matrix. We also specialize the form of the optimal input covariance to the asymptotic regimes of low and high snr. We demonstrate that in the low-snr regime the optimal covariance fully correlates the inputs to better combat noise. In contrast, in the high-snr regime the optimal covariance is diagonal with diagonal elements obeying the generalized mercury/waterfilling power allocation policy. Numerical results illustrate that covariance optimization may lead to significant gains with respect to conventional strategies based on channel diagonalization followed by mercury/waterfilling or waterfilling power allocation, particularly in the regimes of medium and high snr.