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Download fileMachine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO
Preamble
collision is a bottleneck that impairs the performance of random access (RA)
user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging
distributed massive multiple input multiple output (mMIMO) together with
machine learning, a novel machine learning based framework solution is proposed
to address the preamble collision problem in GFRA. The key idea is to identify
and employ the neighboring access points (APs) of a collided RA UE for its data
decoding rather than all the APs, so that the mutual interference among
collided RA UEs can be effectively mitigated. To this end, we first design a
tailored deep neural network (DNN) to enable the preamble multiplicity
estimation in GFRA, where an energy detection (ED) method is also proposed for
performance comparison. With the estimated preamble multiplicity, we then
propose a K-means AP clustering algorithm to cluster the neighboring APs of
collided RA UEs and organize each AP cluster to decode the received data individually.
Simulation results show that a decent performance of preamble multiplicity
estimation in terms of accuracy and reliability can be achieved by the proposed
DNN, and confirm that the proposed schemes are effective in preamble collision
resolution in GFRA, which are able to achieve a near-optimal performance in
terms of uplink achievable rate per collided RA UE, and offer significant
performance improvement over traditional schemes.
History
Email Address of Submitting Author
yxdj2010@gmail.comSubmitting Author's Institution
Deakin UniversitySubmitting Author's Country
- Australia