106,156 research outputs found
Beamspace Aware Adaptive Channel Estimation for Single-Carrier Time-varying Massive MIMO Channels
In this paper, the problem of sequential beam construction and adaptive
channel estimation based on reduced rank (RR) Kalman filtering for
frequency-selective massive multiple-input multiple-output (MIMO) systems
employing single-carrier (SC) in time division duplex (TDD) mode are
considered. In two-stage beamforming, a new algorithm for statistical
pre-beamformer design is proposed for spatially correlated time-varying
wideband MIMO channels under the assumption that the channel is a stationary
Gauss-Markov random process. The proposed algorithm yields a nearly optimal
pre-beamformer whose beam pattern is designed sequentially with low complexity
by taking the user-grouping into account, and exploiting the properties of
Kalman filtering and associated prediction error covariance matrices. The
resulting design, based on the second order statistical properties of the
channel, generates beamspace on which the RR Kalman estimator can be realized
as accurately as possible. It is observed that the adaptive channel estimation
technique together with the proposed sequential beamspace construction shows
remarkable robustness to the pilot interference. This comes with significant
reduction in both pilot overhead and dimension of the pre-beamformer lowering
both hardware complexity and power consumption.Comment: 7 pages, 3 figures, accepted by IEEE ICC 2017 Wireless Communications
Symposiu
An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key
enablers of 6G wireless networks, for which channel estimation is highly
challenging. Traditional analytical estimation methods are no longer effective,
as the enlarged array aperture and the small wavelength result in a mixture of
far-field and near-field paths, constituting a hybrid-field channel. Deep
learning (DL)-based methods, despite the competitive performance, generally
lack theoretical guarantees and scale poorly with the size of the array. In
this paper, we propose a general DL framework for THz UM-MIMO channel
estimation, which leverages existing iterative channel estimators and is with
provable guarantees. Each iteration is implemented by a fixed point network
(FPN), consisting of a closed-form linear estimator and a DL-based non-linear
estimator. The proposed method perfectly matches the THz UM-MIMO channel
estimation due to several unique advantages. First, the complexity is low and
adaptive. It enjoys provable linear convergence with a low per-iteration cost
and monotonically increasing accuracy, which enables an adaptive
accuracy-complexity tradeoff. Second, it is robust to practical distribution
shifts and can directly generalize to a variety of heavily out-of-distribution
scenarios with almost no performance loss, which is suitable for the
complicated THz channel conditions. For practical usage, the proposed framework
is further extended to wideband THz UM-MIMO systems with beam squint effect.
Theoretical analysis and extensive simulation results are provided to
illustrate the advantages over the state-of-the-art methods in estimation
accuracy, convergence rate, complexity, and robustness.Comment: 15 pages, 11 figures, 5 tables, accepted by IEEE Journal of Selected
Topics in Signal Processing (JSTSP
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