178 research outputs found
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning approach
Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising
technology for future wireless communications. The deployment of XL-MIMO,
especially at high-frequency bands, leads to users being located in the
near-field region instead of the conventional far-field. This letter proposes
efficient model-based deep learning algorithms for estimating the near-field
wireless channel of XL-MIMO communications. In particular, we first formulate
the XL-MIMO near-field channel estimation task as a compressed sensing problem
using the spatial gridding-based sparsifying dictionary, and then solve the
resulting problem by applying the Learning Iterative Shrinkage and Thresholding
Algorithm (LISTA). Due to the near-field characteristic, the spatial
gridding-based sparsifying dictionary may result in low channel estimation
accuracy and a heavy computational burden. To address this issue, we further
propose a new sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that
formulates the sparsifying dictionary as a neural network layer and embeds it
into LISTA neural network. The numerical results show that our proposed
algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves
better performance than LISTA with ten times atoms reduction.Comment: 4 pages, 5 figure
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