### 2020

Asyhari, Taufiq A; Koch, Tobias; i Fàbregas, Albert Guillén

Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels Artículo de revista

En: Entropy, vol. 22, no. 9, pp. 971, 2020.

Enlaces | BibTeX | Etiquetas: achievable rates, Fading, high signal-to-noise ratio (SNR), mismatched decoding, multiple antennas, multiple-access channels, nearest neighbor decoding, noncoherent, pilot-aided channel estimation

@article{Tobi20b,

title = {Nearest Neighbor Decoding and Pilot-Aided Channel Estimation for Fading Channels},

author = {Taufiq A Asyhari and Tobias Koch and Albert Guill\'{e}n i F\`{a}bregas},

doi = {https://doi.org/10.3390/e22090971},

year = {2020},

date = {2020-08-31},

journal = {Entropy},

volume = {22},

number = {9},

pages = {971},

keywords = {achievable rates, Fading, high signal-to-noise ratio (SNR), mismatched decoding, multiple antennas, multiple-access channels, nearest neighbor decoding, noncoherent, pilot-aided channel estimation},

pubstate = {published},

tppubtype = {article}

}

### 2010

Koch, Tobias; Lapidoth, Amos

Gaussian Fading Is the Worst Fading Artículo de revista

En: IEEE Transactions on Information Theory, vol. 56, no. 3, pp. 1158–1165, 2010, ISSN: 0018-9448.

Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters

@article{Koch2010a,

title = {Gaussian Fading Is the Worst Fading},

author = {Tobias Koch and Amos Lapidoth},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5429105},

issn = {0018-9448},

year = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {56},

number = {3},

pages = {1158--1165},

abstract = {The capacity of peak-power limited, single-antenna, noncoherent, flat-fading channels with memory is considered. The emphasis is on the capacity pre-log, i.e., on the limiting ratio of channel capacity to the logarithm of the signal-to-noise ratio (SNR), as the SNR tends to infinity. It is shown that, among all stationary and ergodic fading processes of a given spectral distribution function and whose law has no mass point at zero, the Gaussian process gives rise to the smallest pre-log. The assumption that the law of the fading process has no mass point at zero is essential in the sense that there exist stationary and ergodic fading processes whose law has a mass point at zero and that give rise to a smaller pre-log than the Gaussian process of equal spectral distribution function. An extension of these results to multiple-input single-output (MISO) fading channels with memory is also presented.},

keywords = {Additive noise, channel capacity, channels with memory, Distribution functions, ergodic fading processes, Fading, fading channels, flat fading, flat-fading channel capacity, Gaussian channels, Gaussian fading, Gaussian processes, H infinity control, high signal-to-noise ratio (SNR), Information technology, information theory, multiple-input single-output fading channels, multiplexing gain, noncoherent, noncoherent channel capacity, peak-power limited channel capacity, Signal to noise ratio, signal-to-noise ratio, single-antenna channel capacity, spectral distribution function, time-selective, Transmitters},

pubstate = {published},

tppubtype = {article}

}

### 2009

Koch, Tobias; Lapidoth, Amos; Sotiriadis, Paul P

Channels That Heat Up Artículo de revista

En: IEEE Transactions on Information Theory, vol. 55, no. 8, pp. 3594–3612, 2009, ISSN: 0018-9448.

Resumen | Enlaces | BibTeX | Etiquetas: additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise

@article{Koch2009,

title = {Channels That Heat Up},

author = {Tobias Koch and Amos Lapidoth and Paul P Sotiriadis},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5165190},

issn = {0018-9448},

year = {2009},

date = {2009-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {55},

number = {8},

pages = {3594--3612},

abstract = {This paper considers an additive noise channel where the time-A; noise variance is a weighted sum of the squared magnitudes of the previous channel inputs plus a constant. This channel model accounts for the dependence of the intrinsic thermal noise on the data due to the heat dissipation associated with the transmission of data in electronic circuits: the data determine the transmitted signal, which in turn heats up the circuit and thus influences the power of the thermal noise. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-versus-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.},

keywords = {additive noise channel, Capacity per unit cost, channel capacity, channels with memory, cooling, electronic circuits, heat dissipation, heat sinks, high signal-to-noise ratio, high signal-to-noise ratio (SNR), intrinsic thermal noise, low transmit power, network analysis, noise variance, on-chip communication, thermal noise},

pubstate = {published},

tppubtype = {article}

}