2 research outputs found
A simpler derivation of the coding theorem
A simple proof for the Shannon coding theorem, using only the Markov
inequality, is presented. The technique is useful for didactic purposes, since
it does not require many preliminaries and the information density and mutual
information follow naturally in the proof. It may also be applicable to
situations where typicality is not natural
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
Previous studies have demonstrated that end-to-end learning enables
significant shaping gains over additive white Gaussian noise (AWGN) channels.
However, its benefits have not yet been quantified over realistic wireless
channel models. This work aims to fill this gap by exploring the gains of
end-to-end learning over a frequency- and time-selective fading channel using
orthogonal frequency division multiplexing (OFDM). With imperfect channel
knowledge at the receiver, the shaping gains observed on AWGN channels vanish.
Nonetheless, we identify two other sources of performance improvements. The
first comes from a neural network (NN)-based receiver operating over a large
number of subcarriers and OFDM symbols which allows to significantly reduce the
number of orthogonal pilots without loss of bit error rate (BER). The second
comes from entirely eliminating orthognal pilots by jointly learning a neural
receiver together with either superimposed pilots (SIPs), linearly combined
with conventional quadrature amplitude modulation (QAM), or an optimized
constellation geometry. The learned geometry works for a wide range of
signal-to-noise ratios (SNRs), Doppler and delay spreads, has zero mean and
does hence not contain any form of superimposed pilots. Both schemes achieve
the same BER as the pilot-based baseline with around 7% higher throughput.
Thus, we believe that a jointly learned transmitter and receiver are a very
interesting component for beyond-5G communication systems which could remove
the need and associated control overhead for demodulation reference signals
(DMRSs)