2 research outputs found
Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO with One-Dimensional Antenna Array
In this paper, we study blind channel-and-signal estimation by exploiting the
burst-sparse structure of angular-domain propagation channels in massive MIMO
systems. The state-of-the-art approach utilizes the structured channel sparsity
by sampling the angular-domain channel representation with a uniform
angle-sampling grid, a.k.a. virtual channel representation. However, this
approach is only applicable to uniform linear arrays and may cause a
substantial performance loss due to the mismatch between the virtual
representation and the true angle information. To tackle these challenges, we
propose a sparse channel representation with a super-resolution sampling grid
and a hidden Markovian support. Based on this, we develop a novel approximate
inference based blind estimation algorithm to estimate the channel and the user
signals simultaneously, with emphasis on the adoption of the
expectation-maximization method to learn the angle information. Furthermore, we
demonstrate the low-complexity implementation of our algorithm, making use of
factor graph and message passing principles to compute the marginal posteriors.
Numerical results show that our proposed method significantly reduces the
estimation error compared to the state-of-the-art approach under various
settings, which verifies the efficiency and robustness of our method.Comment: 16 pages, 10 figure
Double-Sparsity Learning Based Channel-and-Signal Estimation in Massive MIMO with Generalized Spatial Modulation
In this paper, we study joint antenna activity detection, channel estimation,
and multiuser detection for massive multiple-input multiple-output (MIMO)
systems with general spatial modulation (GSM). We first establish a
double-sparsity massive MIMO model by considering the channel sparsity of the
massive MIMO channel and the signal sparsity of GSM. Based on the
double-sparsity model, we formulate a blind detection problem. To solve the
blind detection problem, we develop message-passing based blind
channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically
follows the affine sparse matrix factorization technique, but with critical
modifications to handle the double-sparsity property of the model. We show that
the BCSE algorithm significantly outperforms the existing blind and
training-based algorithms, and is able to closely approach the genie bounds
(with either known channel or known signal). In the BCSE algorithm, short
pilots are employed to remove the phase and permutation ambiguities after
sparse matrix factorization. To utilize the short pilots more efficiently, we
further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm
to incorporate the estimation of the phase and permutation ambiguities into the
iterative message-passing process. We show that the SBCSE algorithm
substantially outperforms the counterpart algorithms including the BCSE
algorithm in the short-pilot regime