256 research outputs found
Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model
Massive multiple-input multiple-output (MIMO) precoders are typically
designed by minimizing the transmit power subject to a quality-of-service (QoS)
constraint. However, current sustainability goals incentivize more
energy-efficient solutions and thus it is of paramount importance to minimize
the consumed power directly. Minimizing the consumed power of the power
amplifier (PA), one of the most consuming components, gives rise to a convex,
non-differentiable optimization problem, which has been solved in the past
using conventional convex solvers. Additionally, this problem can be solved
using a proximal gradient descent (PGD) algorithm, which suffers from slow
convergence. In this work, to overcome the slow convergence, a deep unfolded
version of the algorithm is proposed, which can achieve close-to-optimal
solutions in only 20 iterations compared to the 3500 plus iterations needed by
the PGD algorithm. Results indicate that the deep unfolding algorithm is three
orders of magnitude faster than a conventional convex solver and four orders of
magnitude faster than the PGD.Comment: This paper is presented at VTC2023-Spring. T. Feys, X. Mestre, E.
Peschiera, and F. Rottenberg, "Deep Unfolding for Fast Linear Massive MIMO
Precoders under a PA Consumption Model," in 2023 IEEE 97th Vehicular
Technology Conference (VTC2023-Spring), Florence, Italy, June 202
Robust MIMO Detection With Imperfect CSI: A Neural Network Solution
In this paper, we investigate the design of statistically robust detectors
for multi-input multi-output (MIMO) systems subject to imperfect channel state
information (CSI). A robust maximum likelihood (ML) detection problem is
formulated by taking into consideration the CSI uncertainties caused by both
the channel estimation error and the channel variation. To address the
challenging discrete optimization problem, we propose an efficient alternating
direction method of multipliers (ADMM)-based algorithm, which only requires
calculating closed-form solutions in each iteration. Furthermore, a robust
detection network RADMMNet is constructed by unfolding the ADMM iterations and
employing both model-driven and data-driven philosophies. Moreover, in order to
relieve the computational burden, a low-complexity ADMM-based robust detector
is developed using the Gaussian approximation, and the corresponding deep
unfolding network LCRADMMNet is further established. On the other hand, we also
provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking
method, which can effectively refine the CSI accuracy and improve the
performance of the proposed robust detectors. Simulation results validate the
significant performance advantages of the proposed robust detection networks
over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO
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