1,097 research outputs found
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
End-to-end data-driven machine learning (ML) of multiple-input
multiple-output (MIMO) systems has been shown to have the potential of
exceeding the performance of engineered MIMO transceivers, without any a priori
knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and
multi-user MIMO and show that the gains of ML-based communication in the former
two cases can be to a large extent ascribed to implicitly learned geometric
shaping and bit and power allocation, not to learning new spatial encoders. For
MU-MIMO, we demonstrate the feasibility of a novel method with centralized
learning and decentralized executing, outperforming conventional zero-forcing.
For each scenario, we provide explicit descriptions as well as open-source
implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape
Precoded FIR and Redundant V-BLAST Systems for Frequency-Selective MIMO Channels
The vertical Bell labs layered space-time (V-BLAST) system is a multi-input multioutput (MIMO) system designed to achieve good multiplexing gain. In recent literature, a precoder, which exploits channel information, has been added in the V-BLAST transmitter. This precoder forces each symbol stream to have an identical mean square error (MSE). It can be viewed as an alternative to the bit-loading method. In this paper, this precoded V-BLAST system is extended to the case of frequency-selective MIMO channels. Both the FIR and redundant types of transceivers, which use cyclic-prefixing and zero-padding, are considered. A fast algorithm for computing a cyclic-prefixing-based precoded V-BLAST transceiver is developed. Experiments show that the proposed methods with redundancy have better performance than the SVD-based system with optimal powerloading and bit loading for frequency-selective MIMO channels. The gain comes from the fact that the MSE-equalizing precoder has better bit-error rate performance than the optimal bitloading method
Limiting Performance of Conventional and Widely Linear DFT-precoded-OFDM Receivers in Wideband Frequency Selective Channels
This paper describes the limiting behavior of linear and decision feedback
equalizers (DFEs) in single/multiple antenna systems employing
real/complex-valued modulation alphabets. The wideband frequency selective
channel is modeled using a Rayleigh fading channel model with infinite number
of time domain channel taps. Using this model, we show that the considered
equalizers offer a fixed post signal-to-noise-ratio (post-SNR) at the equalizer
output that is close to the matched filter bound (MFB). General expressions for
the post-SNR are obtained for zero-forcing (ZF) based conventional receivers as
well as for the case of receivers employing widely linear (WL) processing.
Simulation is used to study the bit error rate (BER) performance of both MMSE
and ZF based receivers. Results show that the considered receivers
advantageously exploit the rich frequency selective channel to mitigate both
fading and inter-symbol-interference (ISI) while offering a performance
comparable to the MFB
MIMO Transceivers With Decision Feedback and Bit Loading: Theory and Optimization
This paper considers MIMO transceivers with linear precoders and decision feedback equalizers (DFEs), with bit allocation at the transmitter. Zero-forcing (ZF) is assumed. Considered first is the minimization of transmitted power, for a given total bit rate and a specified set of error probabilities for the symbol streams. The precoder and DFE matrices are optimized jointly with bit allocation. It is shown that the generalized triangular decomposition (GTD) introduced by Jiang, Li, and Hager offers an optimal family of solutions. The optimal linear transceiver (which has a linear equalizer rather than a DFE) with optimal bit allocation is a member of this family. This shows formally that, under optimal bit allocation, linear and DFE transceivers achieve the same minimum power. The DFE transceiver using the geometric mean decomposition (GMD) is another member of this optimal family, and is such that optimal bit allocation yields identical bits for all symbol streams—no bit allocation is necessary—when the specified error probabilities are identical for all streams. The QR-based system used in VBLAST is yet another member of the optimal family and is particularly well-suited when limited feedback is allowed from receiver to transmitter. Two other optimization problems are then considered: a) minimization of power for specified set of bit rates and error probabilities (the QoS problem), and b) maximization of bit rate for fixed set of error probabilities and power. It is shown in both cases that the GTD yields an optimal family of solutions
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