3,921 research outputs found
Group-blind detection with very large antenna arrays in the presence of pilot contamination
Massive MIMO is, in general, severely affected by pilot contamination. As
opposed to traditional detectors, we propose a group-blind detector that takes
into account the presence of pilot contamination. While sticking to the
traditional structure of the training phase, where orthogonal pilot sequences
are reused, we use the excess antennas at each base station to partially remove
interference during the uplink data transmission phase. We analytically derive
the asymptotic SINR achievable with group-blind detection, and confirm our
findings by simulations. We show, in particular, that in an
interference-limited scenario with one dominant interfering cell, the SINR can
be doubled compared to non-group-blind detection.Comment: 5 pages, 4 figure
Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code
The 3D MIMO code is a robust and efficient space-time block code (STBC) for
the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML)
decoding complexity. In this paper, we first analyze some properties of the 3D
MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that
the ML decoding performance can be achieved with a complexity of O(M^{4.5})
instead of O(M^8) in quasi static channel with M-ary square QAM modulations.
Consequently, we propose a simplified ML decoder exploiting the unique
properties of 3D MIMO code. Simulation results show that the proposed
simplified ML decoder can achieve much lower processing time latency compared
to the classical sphere decoder with Schnorr-Euchner enumeration
Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks
In this paper, we investigate a spectrum sensing algorithm for detecting
spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO)
transmissions for OFDM systems using Compressive Sensing (CS) tools. This
extends the energy detector to allow for detecting transmission opportunities
even if the band is already energy filled. We show that the task described
above is not performed efficiently by regular MIMO decoders (such as MMSE
decoder) due to possible sparsity in the transmit signal. Since CS
reconstruction tools take into account the sparsity order of the signal, they
are more efficient in detecting the activity of the users. Building on
successful activity detection by the CS detector, we show that the use of a
CS-aided MMSE decoders yields better performance rather than using either
CS-based or MMSE decoders separately. Simulations are conducted to verify the
gains from using CS detector for Primary user activity detection and the
performance gain in using CS-aided MMSE decoders for decoding the PU
information for future relaying.Comment: accepted for PIMRC 201
Joint data detection and channel estimation for OFDM systems
We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers from rank deficiency; for this, we give a low-complexity solution. Avoiding a computationally prohibitive exhaustive search, we solve our data detectors using sphere decoding (SD) and V-BLAST and provide simple adaptations of the SD algorithm. We consider how the blind detector performs under mismatch, generalize the basic data detectors to nonunitary constellations, and extend them to systems with pilots and virtual carriers. Simulations show that our data detectors perform well
Robust massive MIMO Equilization for mmWave systems with low resolution ADCs
Leveraging the available millimeter wave spectrum will be important for 5G.
In this work, we investigate the performance of digital beamforming with low
resolution ADCs based on link level simulations including channel estimation,
MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR
type 1 OFDM reference signals. The comparison shows sequential DCD outperforms
MMSE-based MIMO equalization both in terms of detection performance and
complexity. We also show that the DCD based algorithm is more robust to channel
estimation errors. In contrast to the common believe we also show that the
complexity of MMSE equalization for a massive MIMO system is not dominated by
the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
This paper surveys recent advances in the area of very large MIMO systems.
With very large MIMO, we think of systems that use antenna arrays with an
order of magnitude more elements than in systems being built today, say a
hundred antennas or more. Very large MIMO entails an unprecedented number of
antennas simultaneously serving a much smaller number of terminals. The
disparity in number emerges as a desirable operating condition and a practical
one as well. The number of terminals that can be simultaneously served is
limited, not by the number of antennas, but rather by our inability to acquire
channel-state information for an unlimited number of terminals. Larger numbers
of terminals can always be accommodated by combining very large MIMO technology
with conventional time- and frequency-division multiplexing via OFDM. Very
large MIMO arrays is a new research field both in communication theory,
propagation, and electronics and represents a paradigm shift in the way of
thinking both with regards to theory, systems and implementation. The ultimate
vision of very large MIMO systems is that the antenna array would consist of
small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine,
October 201
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