26 research outputs found
A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO
Employing low resolution analog-to-digital converters in massive
multiple-input multiple-output (MIMO) has many advantages in terms of total
power consumption, cost and feasibility of such systems. However, such
advantages come together with significant challenges in channel estimation and
data detection due to the severe quantization noise present. In this study, we
propose a novel iterative receiver for quantized uplink single carrier MIMO
(SC-MIMO) utilizing an efficient message passing algorithm based on the
Bussgang decomposition and Ungerboeck factorization, which avoids the use of a
complex whitening filter. A reduced state sequence estimator with bidirectional
decision feedback is also derived, achieving remarkable complexity reduction
compared to the existing receivers for quantized SC-MIMO in the literature,
without any requirement on the sparsity of the transmission channel. Moreover,
the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO
under frequency-selective channel, which do not require any cyclic-prefix
overhead, is also derived. We observe that the proposed receiver has
significant performance gains with respect to the existing receivers in the
literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication.
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Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
Base station (BS) architectures for massive multi-user (MU) multiple-input
multiple-output (MIMO) wireless systems are equipped with hundreds of antennas
to serve tens of users on the same time-frequency channel. The immense number
of BS antennas incurs high system costs, power, and interconnect bandwidth. To
circumvent these obstacles, sophisticated MU precoding algorithms that enable
the use of 1-bit DACs have been proposed. Many of these precoders feature
parameters that are, traditionally, tuned manually to optimize their
performance. We propose to use deep-learning tools to automatically tune such
1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO)
algorithm using neural networks. Compared to the original C2PO algorithm, our
neural-network optimized (NNO-)C2PO achieves the same error-rate performance at
lower complexity. Moreover, by training NNO-C2PO for
different channel models, we show that 1-bit precoding can be made robust to
vastly changing propagation conditions
Review of Recent Trends
This work was partially supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework, through projects SOCA (CENTRO-01-0145-FEDER-000010) and ORCIP (CENTRO-01-0145-FEDER-022141). Fernando P. Guiomar acknowledges a fellowship from “la Caixa” Foundation (ID100010434), code LCF/BQ/PR20/11770015. Houda Harkat acknowledges the financial support of the Programmatic Financing of the CTS R&D Unit (UIDP/00066/2020).MIMO-OFDM is a key technology and a strong candidate for 5G telecommunication systems. In the literature, there is no convenient survey study that rounds up all the necessary points to be investigated concerning such systems. The current deeper review paper inspects and interprets the state of the art and addresses several research axes related to MIMO-OFDM systems. Two topics have received special attention: MIMO waveforms and MIMO-OFDM channel estimation. The existing MIMO hardware and software innovations, in addition to the MIMO-OFDM equalization techniques, are discussed concisely. In the literature, only a few authors have discussed the MIMO channel estimation and modeling problems for a variety of MIMO systems. However, to the best of our knowledge, there has been until now no review paper specifically discussing the recent works concerning channel estimation and the equalization process for MIMO-OFDM systems. Hence, the current work focuses on analyzing the recently used algorithms in the field, which could be a rich reference for researchers. Moreover, some research perspectives are identified.publishersversionpublishe