5,950 research outputs found
Power Scaling of Uplink Massive MIMO Systems with Arbitrary-Rank Channel Means
This paper investigates the uplink achievable rates of massive multiple-input
multiple-output (MIMO) antenna systems in Ricean fading channels, using
maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect
and imperfect channel state information (CSI). In contrast to previous relevant
works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank
deterministic component as well as a Rayleigh-distributed random component. We
derive tractable expressions for the achievable uplink rate in the
large-antenna limit, along with approximating results that hold for any finite
number of antennas. Based on these analytical results, we obtain the scaling
law that the users' transmit power should satisfy, while maintaining a
desirable quality of service. In particular, it is found that regardless of the
Ricean -factor, in the case of perfect CSI, the approximations converge to
the same constant value as the exact results, as the number of base station
antennas, , grows large, while the transmit power of each user can be scaled
down proportionally to . If CSI is estimated with uncertainty, the same
result holds true but only when the Ricean -factor is non-zero. Otherwise,
if the channel experiences Rayleigh fading, we can only cut the transmit power
of each user proportionally to . In addition, we show that with an
increasing Ricean -factor, the uplink rates will converge to fixed values
for both MRC and ZF receivers
Power Allocation Schemes for Multicell Massive MIMO Systems
This paper investigates the sum-rate gains brought by power allocation
strategies in multicell massive multipleinput multiple-output systems, assuming
time-division duplex transmission. For both uplink and downlink, we derive
tractable expressions for the achievable rate with zero-forcing receivers and
precoders respectively. To avoid high complexity joint optimization across the
network, we propose a scheduling mechanism for power allocation, where in a
single time slot, only cells that do not interfere with each other adjust their
transmit powers. Based on this, corresponding transmit power allocation
strategies are derived, aimed at maximizing the sum rate per-cell. These
schemes are shown to bring considerable gains over equal power allocation for
practical antenna configurations (e.g., up to a few hundred). However, with
fixed number of users (N), these gains diminish as M turns to infinity, and
equal power allocation becomes optimal. A different conclusion is drawn for the
case where both M and N grow large together, in which case: (i) improved rates
are achieved as M grows with fixed M/N ratio, and (ii) the relative gains over
the equal power allocation diminish as M/N grows. Moreover, we also provide
applicable values of M/N under an acceptable power allocation gain threshold,
which can be used as to determine when the proposed power allocation schemes
yield appreciable gains, and when they do not. From the network point of view,
the proposed scheduling approach can achieve almost the same performance as the
joint power allocation after one scheduling round, with much reduced
complexity
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Representation learning on networks aims to derive a meaningful vector
representation for each node, thereby facilitating downstream tasks such as
link prediction, node classification, and node clustering. In heterogeneous
text-rich networks, this task is more challenging due to (1) presence or
absence of text: Some nodes are associated with rich textual information, while
others are not; (2) diversity of types: Nodes and edges of multiple types form
a heterogeneous network structure. As pretrained language models (PLMs) have
demonstrated their effectiveness in obtaining widely generalizable text
representations, a substantial amount of effort has been made to incorporate
PLMs into representation learning on text-rich networks. However, few of them
can jointly consider heterogeneous structure (network) information as well as
rich textual semantic information of each node effectively. In this paper, we
propose Heterformer, a Heterogeneous Network-Empowered Transformer that
performs contextualized text encoding and heterogeneous structure encoding in a
unified model. Specifically, we inject heterogeneous structure information into
each Transformer layer when encoding node texts. Meanwhile, Heterformer is
capable of characterizing node/edge type heterogeneity and encoding nodes with
or without texts. We conduct comprehensive experiments on three tasks (i.e.,
link prediction, node classification, and node clustering) on three large-scale
datasets from different domains, where Heterformer outperforms competitive
baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer
Robust offline reinforcement learning with heavy-tailed rewards
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applications. We propose two algorithmic frameworks, ROAM and ROOM, for robust off-policy evaluation and offline policy optimization (OPO), respectively. Central to our frameworks is the strategic incorporation of the median-of-means method with offline RL, enabling straightforward uncertainty estimation for the value function estimator. This not only adheres to the principle of pessimism in OPO but also adeptly manages heavytailed rewards. Theoretical results and extensive experiments demonstrate that our two frameworks outperform existing methods on the logged dataset exhibits heavytailed reward distributions. The implementation of the proposal is available at https: //github.com/Mamba413/ROOM
Robust Offline Policy Evaluation and Optimization with Heavy-Tailed Rewards
This paper endeavors to augment the robustness of offline reinforcement
learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent
circumstance in real-world applications. We propose two algorithmic frameworks,
ROAM and ROOM, for robust off-policy evaluation (OPE) and offline policy
optimization (OPO), respectively. Central to our frameworks is the strategic
incorporation of the median-of-means method with offline RL, enabling
straightforward uncertainty estimation for the value function estimator. This
not only adheres to the principle of pessimism in OPO but also adeptly manages
heavy-tailed rewards. Theoretical results and extensive experiments demonstrate
that our two frameworks outperform existing methods on the logged dataset
exhibits heavy-tailed reward distributions
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is
a pressing need to tag each paper with fine-grained topics so that researchers
can track their interested fields of study rather than drowning in the whole
literature. Scientific literature tagging is beyond a pure multi-label text
classification task because papers on the Web are prevalently accompanied by
metadata information such as venues, authors, and references, which may serve
as additional signals to infer relevant tags. Although there have been studies
making use of metadata in academic paper classification, their focus is often
restricted to one or two scientific fields (e.g., computer science and
biomedicine) and to one specific model. In this work, we systematically study
the effect of metadata on scientific literature tagging across 19 fields. We
select three representative multi-label classifiers (i.e., a bag-of-words
model, a sequence-based model, and a pre-trained language model) and explore
their performance change in scientific literature tagging when metadata are fed
to the classifiers as additional features. We observe some ubiquitous patterns
of metadata's effects across all fields (e.g., venues are consistently
beneficial to paper tagging in almost all cases), as well as some unique
patterns in fields other than computer science and biomedicine, which are not
explored in previous studies.Comment: 11 pages; Accepted to WWW 202
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