![Institutional Repository [SANDBOX]](images/dias.logo.png "Institutional Repository [SANDBOX]"){kind=logo}
Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/09D9E7D7-87E2-4661-A6FE-6963C6BB6335 | ||
| Year | 2013 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | D. S. Papailiopoulos, G. Abou Elkheir, and G. N. Karystinos,"Maximum-likelihood noncoherent PAM detection," IEEE Transactions on Communications, vol. 61, no. 3, pp. 1152 - 1159, Mar. 2013. doi: 10.1109/TCOMM.2012.010913.120448 https://doi.org/10.1109/TCOMM.2012.010913.120448 | ||
| Appears in Collections |
Sequence detection offers improved error-rate performance over conventional symbol-by-symbol detection when channel knowledge is not available at the receiver end. However, maximum-likelihood (ML) noncoherent sequence detection is proven to be notoriously intractable in many communication settings. In this work, we develop a new ML sequence detector for pulse-amplitude modulation (PAM) or quadrature-amplitude modulation (QAM) transmissions in unknown Rayleigh fading. Our detector identifies the ML sequence with overall polynomial complexity. This is possible via an auxiliary-angle approach that unlocks a low-rank property of the ML detection problem, reduces the exponential-size set of solution sequences to a polynomial-size set of candidates, and guarantees that the ML sequence is always contained in this substantially smaller set.
Copyright © DIAS 2013
