1,121 research outputs found
Extreme Learning Machine-Based Receiver for MIMO LED Communications
This work concerns receiver design for light-emitting diode (LED) multiple
input multiple output (MIMO) communications where the LED nonlinearity can
severely degrade the performance of communications. In this paper, we propose
an extreme learning machine (ELM) based receiver to jointly handle the LED
nonlinearity and cross-LED interference, and a circulant input weight matrix is
employed, which significantly reduces the complexity of the receiver with the
fast Fourier transform (FFT). It is demonstrated that the proposed receiver can
efficiently handle the LED nonlinearity and cross-LED interference
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Hybrid massive MIMO structures with lower hardware complexity and power
consumption have been considered as a potential candidate for millimeter wave
(mmWave) communications. Channel covariance information can be used for
designing transmitter precoders, receiver combiners, channel estimators, etc.
However, hybrid structures allow only a lower-dimensional signal to be
observed, which adds difficulties for channel covariance matrix estimation. In
this paper, we formulate the channel covariance estimation as a structured
low-rank matrix sensing problem via Kronecker product expansion and use a
low-complexity algorithm to solve this problem. Numerical results with uniform
linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to
demonstrate the effectiveness of our proposed method
A low-complexity iterative channel estimation and detection technique for doubly selective channels
In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key is a segment-by-segment frequency domain equalization (FDE) strategy under the assumption that channel is approximately static within a short segment. Guard gaps (for cyclic prefixing or zero padding) are not required between adjacent segments, which avoids the power and spectral overheads due to the use of cyclic prefix (CP) in the conventional FDE technique. A low-complexity bi-directional channel estimation algorithm is also developed to exploit correlation information of time-varying channels. Simulation results are provided to demonstrate the efficiency of the proposed algorithms. © 2008 IEEE
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
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A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
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