72 research outputs found
A Novel Uplink Data Transmission Scheme For Small Packets In Massive MIMO System
Intelligent terminals often produce a large number of data packets of small
lengths. For these packets, it is inefficient to follow the conventional medium
access control (MAC) protocols because they lead to poor utilization of service
resources. We propose a novel multiple access scheme that targets massive
multiple-input multiple-output (MIMO) systems based on compressive sensing
(CS). We employ block precoding in the time domain to enable the simultaneous
transmissions of many users, which could be even more than the number of
receive antennas at the base station. We develop a block-sparse system model
and adopt the block orthogonal matching pursuit (BOMP) algorithm to recover the
transmitted signals. Conditions for data recovery guarantees are identified and
numerical results demonstrate that our scheme is efficient for uplink small
packet transmission.Comment: IEEE/CIC ICCC 2014 Symposium on Signal Processing for Communication
Multiple Access for Small Packets Based on Precoding and Sparsity-Aware Detection
Modern mobile terminals often produce a large number of small data packets.
For these packets, it is inefficient to follow the conventional medium access
control protocols because of poor utilization of service resources. We propose
a novel multiple access scheme that employs block-spreading based precoding at
the transmitters and sparsity-aware detection schemes at the base station. The
proposed scheme is well suited for the emerging massive multiple-input
multiple-output (MIMO) systems, as well as conventional cellular systems with a
small number of base-station antennas. The transmitters employ precoding in
time domain to enable the simultaneous transmissions of many users, which could
be even more than the number of receive antennas at the base station. The
system is modeled as a linear system of equations with block-sparse unknowns.
We first adopt the block orthogonal matching pursuit (BOMP) algorithm to
recover the transmitted signals. We then develop an improved algorithm, named
interference cancellation BOMP (ICBOMP), which takes advantage of error
correction and detection coding to perform perfect interference cancellation
during each iteration of BOMP algorithm. Conditions for guaranteed data
recovery are identified. The simulation results demonstrate that the proposed
scheme can accommodate more simultaneous transmissions than conventional
schemes in typical small-packet transmission scenarios.Comment: submitted to IEEE Transactions on Wireless Communication
Many Access for Small Packets Based on Precoding and Sparsity-aware Recovery
Modern mobile terminals produce massive small data packets. For these
short-length packets, it is inefficient to follow the current multiple access
schemes to allocate transmission resources due to heavy signaling overhead. We
propose a non-orthogonal many-access scheme that is well suited for the future
communication systems equipped with many receive antennas. The system is
modeled as having a block-sparsity pattern with unknown sparsity level (i.e.,
unknown number of transmitted messages). Block precoding is employed at each
single-antenna transmitter to enable the simultaneous transmissions of many
users. The number of simultaneously served active users is allowed to be even
more than the number of receive antennas. Sparsity-aware recovery is designed
at the receiver for joint user detection and symbol demodulation. To reduce the
effects of channel fading on signal recovery, normalized block orthogonal
matching pursuit (BOMP) algorithm is introduced, and based on its approximate
performance analysis, we develop interference cancellation based BOMP (ICBOMP)
algorithm. The ICBOMP performs error correction and detection in each iteration
of the normalized BOMP. Simulation results demonstrate the effectiveness of the
proposed scheme in small packet services, as well as the advantages of ICBOMP
in improving signal recovery accuracy and reducing computational cost.Comment: 30 pages 8 figures ,submited to tco
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs
Inductive link prediction -- where entities during training and inference
stages can be different -- has been shown to be promising for completing
continuously evolving knowledge graphs. Existing models of inductive reasoning
mainly focus on predicting missing links by learning logical rules. However,
many existing approaches do not take into account semantic correlations between
relations, which are commonly seen in real-world knowledge graphs. To address
this challenge, we propose a novel inductive reasoning approach, namely TACT,
which can effectively exploit Topology-Aware CorrelaTions between relations in
an entity-independent manner. TACT is inspired by the observation that the
semantic correlation between two relations is highly correlated to their
topological structure in knowledge graphs. Specifically, we categorize all
relation pairs into several topological patterns, and then propose a Relational
Correlation Network (RCN) to learn the importance of the different patterns for
inductive link prediction. Experiments demonstrate that TACT can effectively
model semantic correlations between relations, and significantly outperforms
existing state-of-the-art methods on benchmark datasets for the inductive link
prediction task.Comment: Accepted to AAAI 202
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