1,775 research outputs found
A Low Complexity Pilot Scheduling Algorithm for Massive MIMO
Pilot contamination is a fundamental bottleneck in massive multiple-input multiple-output (MIMO) cellular networks. In this letter, we aim to design a pilot scheduling method to reduce the effect of pilot contamination in multi-user multi-cell massive MIMO systems. Mathematically, the pilot scheduling problem can be formulated as a permutation-based optimization problem. However, finding the optimal solution requires an exhaustive search and is computationally prohibitive. Therefore, we propose a low-complexity near-optimal algorithm developed from the cross-entropy optimization framework to solve this problem. Simulation results reveal that our algorithm not only significantly outperforms the existing pilot-scheduling schemes but also achieves excellent performance with low complexity
Channel Estimation for Massive MIMO-OFDM Systems by Tracking the Joint Angle-Delay Subspace
In this paper, we propose joint angle-delay subspace based channel estimation in single cell for broadband massive multiple-input and multiple-output (MIMO) systems employing orthogonal frequency division multiplexing (OFDM) modulation. Based on a parametric channel model, we present a new concept of the joint angle-delay subspace which can be tracked by the low-complexity low-rank adaptive filtering (LORAF) algorithm. Then, we investigate an interference-free transmission condition that the joint angle-delay subspaces of the users reusing the same pilots are non-overlapping. Since the channel statistics are usually unknown, we develop a robust minimum mean square error (MMSE) estimator under the worst precondition of pilot decontamination, considering that the joint angle-delay subspaces of the interfering users fully overlap. Furthermore, motivated by the interference-free transmission criteria, we present a novel low-complexity greedy pilot scheduling algorithm to avoid the problem of initial value sensitivity. Simulation results show that the joint angle-delay subspace can be estimated effectively, and the proposed pilot reuse scheme combined with robust MMSE channel estimation offers significant performance gains
A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks
Massive MIMO is a promising technology to connect very large numbers of
energy constrained nodes, as it offers both extensive spatial multiplexing and
large array gain. A challenge resides in partitioning the many nodes in groups
that can communicate simultaneously such that the mutual interference is
minimized. We here propose node partitioning strategies that do not require
full channel state information, but rather are based on nodes' respective
directional channel properties. In our considered scenarios, these typically
have a time constant that is far larger than the coherence time of the channel.
We developed both an optimal and an approximation algorithm to partition users
based on directional channel properties, and evaluated them numerically. Our
results show that both algorithms, despite using only these directional channel
properties, achieve similar performance in terms of the minimum
signal-to-interference-plus-noise ratio for any user, compared with a reference
method using full channel knowledge. In particular, we demonstrate that
grouping nodes with related directional properties is to be avoided. We hence
realise a simple partitioning method requiring minimal information to be
collected from the nodes, and where this information typically remains stable
over a long term, thus promoting their autonomy and energy efficiency
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