1,603 research outputs found
Near-optimal pilot allocation in sparse channel estimation for massive MIMO OFDM systems
Inspired by the success in sparse signal recovery, compressive sensing has already been applied for the pilot-based channel estimation in massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. However, little attention has been paid to the pilot design in the massive MIMO system. To obtain the near-optimal pilot placement, two efficient schemes based on the block coherence (BC) of the measurement matrix are introduced. The first scheme searches the pilot pattern with the minimum BC value through the simultaneous perturbation stochastic approximation (SPSA) method. The second scheme combines the BC with probability model and then utilizes the cross-entropy optimization (CEO) method to solve the pilot allocation problem. Simulation results show that both of the methods outperform the equispaced search method, exhausted search method and random search method in terms of mean square error (MSE) of the channel estimate. Moreover, it is demonstrated that SPSA converges much faster than the other methods thus are more efficient, while CEO could provide more accurate channel estimation performance
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study
In this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble
consisting of equipowered and equispaced pilots embedded in zeros, turns out to
perform at least as well as CP-OFDM. Simulations results are presented that
verify the analysis
Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels
Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid
precoding is challenging, since the number of radio frequency (RF) chains is
usually much smaller than that of antennas. To date, several channel estimation
schemes have been proposed for mmWave massive MIMO over narrow-band channels,
while practical mmWave channels exhibit the frequency-selective fading (FSF).
To this end, this letter proposes a multi-user uplink channel estimation scheme
for mmWave massive MIMO over FSF channels. Specifically, by exploiting the
angle-domain structured sparsity of mmWave FSF channels, a distributed
compressive sensing (DCS)-based channel estimation scheme is proposed.
Moreover, by using the grid matching pursuit strategy with adaptive measurement
matrix, the proposed algorithm can solve the power leakage problem caused by
the continuous angles of arrival or departure (AoA/AoD). Simulation results
verify that the good performance of the proposed solution.Comment: 4 pages, 3 figures, accepted by IEEE Communications Letters. This
paper may be the first one that investigates the frequency selective fading
channel estimation for mmWave massive MIMO systems with hybrid precoding. Key
words: Millimeter-wave (mmWave) massive MIMO, frequency-selective fading,
channel estimation, compressive sensing, hybrid precodin
Modulated Unit-Norm Tight Frames for Compressed Sensing
In this paper, we propose a compressed sensing (CS) framework that consists
of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a
column-wise orthonormal matrix. We prove that this structure satisfies the
restricted isometry property (RIP) with high probability if the number of
measurements for -sparse signals of length
and if the column-wise orthonormal matrix is bounded. Some existing structured
sensing models can be studied under this framework, which then gives tighter
bounds on the required number of measurements to satisfy the RIP. More
importantly, we propose several structured sensing models by appealing to this
unified framework, such as a general sensing model with arbitrary/determinisic
subsamplers, a fast and efficient block compressed sensing scheme, and
structured sensing matrices with deterministic phase modulations, all of which
can lead to improvements on practical applications. In particular, one of the
constructions is applied to simplify the transceiver design of CS-based channel
estimation for orthogonal frequency division multiplexing (OFDM) systems.Comment: submitted to IEEE Transactions on Signal Processin
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