TY - GEN
T1 - Robust transmission over channels with channel uncertainty
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
AU - Boche, Holger
AU - Schaefer, Rafael F.
AU - Vincent Poor, H.
N1 - Funding Information:
This work of H. Boche was supported in part by the German Federal Ministry of Education and Research (BMBF) within the national initiative for “Molecular Communication (MAMOKO)” under Grant 16KIS0914 and in part by the German Research Foundation (DFG) within the Gottfried Wilhelm Leibniz Prize under Grant BO 1734/20-1 and within Germany’s Excellence Strategy – EXC-2111 – 390814868. This work of R. F. Schaefer was supported in part by the NUS/BER Research Partnership Seed Funding Fund and in part by the BMBF within the national initiative for “Post Shannon Communication (NewCom)” under Grant 16KIS1004. This work of H. V. Poor was supported by the U.S. National Science Foundation under Grants CCF-0939370, CCF-1513915, and CCF-1908308.
Publisher Copyright:
© 2020 IEEE
PY - 2020/5
Y1 - 2020/5
N2 - The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes are well studied and often closed-form solutions are known. On the other hand, the case of imperfect channel information is much less understood and closed-form solutions remain unknown in general. This paper approaches this question from a fundamental, algorithmic point of view to study whether or not such optimal schemes can be found algorithmically in principle (without putting any constraints on the computational complexity of such algorithms). To this end, the compound channel is considered as a model for channel uncertainty and it is shown that although the compound channel itself is a computable channel, the corresponding capacity is not computable in general, i.e., there exists no algorithm or Turing machine that takes the channel as an input and computes the corresponding capacity. As an implication of this, it is then shown that for such compound channels, there are no effectively constructible optimal signal processing and coding schemes that achieve the capacity. This is particularly noteworthy as such schemes must exist (since the capacity is known), but they cannot be effectively, i.e., algorithmically, constructed.
AB - The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes are well studied and often closed-form solutions are known. On the other hand, the case of imperfect channel information is much less understood and closed-form solutions remain unknown in general. This paper approaches this question from a fundamental, algorithmic point of view to study whether or not such optimal schemes can be found algorithmically in principle (without putting any constraints on the computational complexity of such algorithms). To this end, the compound channel is considered as a model for channel uncertainty and it is shown that although the compound channel itself is a computable channel, the corresponding capacity is not computable in general, i.e., there exists no algorithm or Turing machine that takes the channel as an input and computes the corresponding capacity. As an implication of this, it is then shown that for such compound channels, there are no effectively constructible optimal signal processing and coding schemes that achieve the capacity. This is particularly noteworthy as such schemes must exist (since the capacity is known), but they cannot be effectively, i.e., algorithmically, constructed.
KW - Channel uncertainty
KW - Optimal coding
KW - Robust communication
KW - Turing computability
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U2 - 10.1109/ICASSP40776.2020.9054467
DO - 10.1109/ICASSP40776.2020.9054467
M3 - Conference contribution
AN - SCOPUS:85085256964
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5230
EP - 5234
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 May 2020 through 8 May 2020
ER -