42 research outputs found
SINR Analysis of Opportunistic MIMO-SDMA Downlink Systems with Linear Combining
Opportunistic scheduling (OS) schemes have been proposed previously by the
authors for multiuser MIMO-SDMA downlink systems with linear combining. In
particular, it has been demonstrated that significant performance improvement
can be achieved by incorporating low-complexity linear combining techniques
into the design of OS schemes for MIMO-SDMA. However, this previous analysis
was performed based on the effective signal-to-interference ratio (SIR),
assuming an interference-limited scenario, which is typically a valid
assumption in SDMA-based systems. It was shown that the limiting distribution
of the effective SIR is of the Frechet type. Surprisingly, the corresponding
scaling laws were found to follow with , rather
than the conventional form.
Inspired by this difference between the scaling law forms, in this paper a
systematic approach is developed to derive asymptotic throughput and scaling
laws based on signal-to-interference-noise ratio (SINR) by utilizing extreme
value theory. The convergence of the limiting distribution of the effective
SINR to the Gumbel type is established. The resulting scaling law is found to
be governed by the conventional form. These novel results are
validated by simulation results. The comparison of SIR and SINR-based analysis
suggests that the SIR-based analysis is more computationally efficient for
SDMA-based systems and it captures the asymptotic system performance with
higher fidelity.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
Opportunistic Scheduling and Beamforming for MIMO-SDMA Downlink Systems with Linear Combining
Opportunistic scheduling and beamforming schemes are proposed for multiuser
MIMO-SDMA downlink systems with linear combining in this work. Signals received
from all antennas of each mobile terminal (MT) are linearly combined to improve
the {\em effective} signal-to-noise-interference ratios (SINRs). By exploiting
limited feedback on the effective SINRs, the base station (BS) schedules
simultaneous data transmission on multiple beams to the MTs with the largest
effective SINRs. Utilizing the extreme value theory, we derive the asymptotic
system throughputs and scaling laws for the proposed scheduling and beamforming
schemes with different linear combining techniques. Computer simulations
confirm that the proposed schemes can substantially improve the system
throughput.Comment: To appear in the Proceedings of the 18th Annual IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC),
Athens, Greece, September 3 - 7, 200
Opportunistic Scheduling and Beamforming for MIMO-OFDMA Downlink Systems with Reduced Feedback
Opportunistic scheduling and beamforming schemes with reduced feedback are
proposed for MIMO-OFDMA downlink systems. Unlike the conventional beamforming
schemes in which beamforming is implemented solely by the base station (BS) in
a per-subcarrier fashion, the proposed schemes take advantages of a novel
channel decomposition technique to perform beamforming jointly by the BS and
the mobile terminal (MT). The resulting beamforming schemes allow the BS to
employ only {\em one} beamforming matrix (BFM) to form beams for {\em all}
subcarriers while each MT completes the beamforming task for each subcarrier
locally. Consequently, for a MIMO-OFDMA system with subcarriers, the
proposed opportunistic scheduling and beamforming schemes require only one BFM
index and supportable throughputs to be returned from each MT to the BS, in
contrast to BFM indices and supportable throughputs required by the
conventional schemes. The advantage of the proposed schemes becomes more
evident when a further feedback reduction is achieved by grouping adjacent
subcarriers into exclusive clusters and returning only cluster information from
each MT. Theoretical analysis and computer simulation confirm the effectiveness
of the proposed reduced-feedback schemes.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
Opportunistic Collaborative Beamforming with One-Bit Feedback
An energy-efficient opportunistic collaborative beamformer with one-bit
feedback is proposed for ad hoc sensor networks over Rayleigh fading channels.
In contrast to conventional collaborative beamforming schemes in which each
source node uses channel state information to correct its local carrier offset
and channel phase, the proposed beamforming scheme opportunistically selects a
subset of source nodes whose received signals combine in a quasi-coherent
manner at the intended receiver. No local phase-precompensation is performed by
the nodes in the opportunistic collaborative beamformer. As a result, each node
requires only one-bit of feedback from the destination in order to determine if
it should or shouldn't participate in the collaborative beamformer. Theoretical
analysis shows that the received signal power obtained with the proposed
beamforming scheme scales linearly with the number of available source nodes.
Since the the optimal node selection rule requires an exhaustive search over
all possible subsets of source nodes, two low-complexity selection algorithms
are developed. Simulation results confirm the effectiveness of opportunistic
collaborative beamforming with the low-complexity selection algorithms.Comment: Proceedings of the Ninth IEEE Workshop on Signal Processing Advances
in Wireless Communications, Recife, Brazil, July 6-9, 200
Distributed Opportunistic Scheduling For Ad-Hoc Communications Under Noisy Channel Estimation
Distributed opportunistic scheduling is studied for wireless ad-hoc networks,
where many links contend for one channel using random access. In such networks,
distributed opportunistic scheduling (DOS) involves a process of joint channel
probing and distributed scheduling. It has been shown that under perfect
channel estimation, the optimal DOS for maximizing the network throughput is a
pure threshold policy. In this paper, this formalism is generalized to explore
DOS under noisy channel estimation, where the transmission rate needs to be
backed off from the estimated rate to reduce the outage. It is shown that the
optimal scheduling policy remains to be threshold-based, and that the rate
threshold turns out to be a function of the variance of the estimation error
and be a functional of the backoff rate function. Since the optimal backoff
rate is intractable, a suboptimal linear backoff scheme that backs off the
estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The
corresponding optimal backoff ratio and rate threshold can be obtained via an
iterative algorithm. Finally, simulation results are provided to illustrate the
tradeoff caused by increasing training time to improve channel estimation at
the cost of probing efficiency.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
Distributed Opportunistic Scheduling for MIMO Ad-Hoc Networks
Distributed opportunistic scheduling (DOS) protocols are proposed for
multiple-input multiple-output (MIMO) ad-hoc networks with contention-based
medium access. The proposed scheduling protocols distinguish themselves from
other existing works by their explicit design for system throughput improvement
through exploiting spatial multiplexing and diversity in a {\em distributed}
manner. As a result, multiple links can be scheduled to simultaneously transmit
over the spatial channels formed by transmit/receiver antennas. Taking into
account the tradeoff between feedback requirements and system throughput, we
propose and compare protocols with different levels of feedback information.
Furthermore, in contrast to the conventional random access protocols that
ignore the physical channel conditions of contending links, the proposed
protocols implement a pure threshold policy derived from optimal stopping
theory, i.e. only links with threshold-exceeding channel conditions are allowed
for data transmission. Simulation results confirm that the proposed protocols
can achieve impressive throughput performance by exploiting spatial
multiplexing and diversity.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
In this work, we develop practical user scheduling algorithms for downlink
bursty traffic with emphasis on user fairness. In contrast to the conventional
scheduling algorithms that either equally divides the transmission time slots
among users or maximizing some ratios without physcial meanings, we propose to
use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness.
Since it is difficult to directly optimize 5TUDR, we first cast the problem
into the stochastic game framework and subsequently propose a Multi-Agent
Reinforcement Learning (MARL)-based algorithm to perform distributed
optimization on the resource block group (RBG) allocation. Furthermore, each
MARL agent is designed to take information measured by network counters from
multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the
input states while the RBG allocation as action with a proposed reward function
designed to maximize 5TUDR. Extensive simulation is performed to show that the
proposed MARL-based scheduler can achieve fair scheduling while maintaining
good average network throughput as compared to conventional schedulers.Comment: 30 pages, 13 figure
DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification
Recent advancements in remote sensing (RS) technologies have shown their
potential in accurately classifying local climate zones (LCZs). However,
traditional scene-level methods using convolutional neural networks (CNNs)
often struggle to integrate prior knowledge of ground objects effectively.
Moreover, commonly utilized data sources like Sentinel-2 encounter difficulties
in capturing detailed ground object information. To tackle these challenges, we
propose a data fusion method that integrates ground object priors extracted
from high-resolution Google imagery with Sentinel-2 multispectral imagery. The
proposed method introduces a novel Dual-stream Fusion framework for LCZ
classification (DF4LCZ), integrating instance-based location features from
Google imagery with the scene-level spatial-spectral features extracted from
Sentinel-2 imagery. The framework incorporates a Graph Convolutional Network
(GCN) module empowered by the Segment Anything Model (SAM) to enhance feature
extraction from Google imagery. Simultaneously, the framework employs a 3D-CNN
architecture to learn the spectral-spatial features of Sentinel-2 imagery.
Experiments are conducted on a multi-source remote sensing image dataset
specifically designed for LCZ classification, validating the effectiveness of
the proposed DF4LCZ. The related code and dataset are available at
https://github.com/ctrlovefly/DF4LCZ
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
The presence of cloud layers severely compromises the quality and
effectiveness of optical remote sensing (RS) images. However, existing
deep-learning (DL)-based Cloud Removal (CR) techniques encounter difficulties
in accurately reconstructing the original visual authenticity and detailed
semantic content of the images. To tackle this challenge, this work proposes to
encompass enhancements at the data and methodology fronts. On the data side, an
ultra-resolution benchmark named CUHK Cloud Removal (CUHK-CR) of 0.5m spatial
resolution is established. This benchmark incorporates rich detailed textures
and diverse cloud coverage, serving as a robust foundation for designing and
assessing CR models. From the methodology perspective, a novel diffusion-based
framework for CR called Diffusion Enhancement (DE) is proposed to perform
progressive texture detail recovery, which mitigates the training difficulty
with improved inference accuracy. Additionally, a Weight Allocation (WA)
network is developed to dynamically adjust the weights for feature fusion,
thereby further improving performance, particularly in the context of
ultra-resolution image generation. Furthermore, a coarse-to-fine training
strategy is applied to effectively expedite training convergence while reducing
the computational complexity required to handle ultra-resolution images.
Extensive experiments on the newly established CUHK-CR and existing datasets
such as RICE confirm that the proposed DE framework outperforms existing
DL-based methods in terms of both perceptual quality and signal fidelity
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Semantic segmentation of remote sensing imagery plays a pivotal role in
extracting precise information for diverse down-stream applications. Recent
development of the Segment Anything Model (SAM), an advanced general-purpose
segmentation model, has revolutionized this field, presenting new avenues for
accurate and efficient segmentation. However, SAM is limited to generating
segmentation results without class information. Consequently, the utilization
of such a powerful general vision model for semantic segmentation in remote
sensing images has become a focal point of research. In this paper, we present
a streamlined framework aimed at leveraging the raw output of SAM by exploiting
two novel concepts called SAM-Generated Object (SGO) and SAM-Generated Boundary
(SGB). More specifically, we propose a novel object loss and further introduce
a boundary loss as augmentative components to aid in model optimization in a
general semantic segmentation framework. Taking into account the content
characteristics of SGO, we introduce the concept of object consistency to
leverage segmented regions lacking semantic information. By imposing
constraints on the consistency of predicted values within objects, the object
loss aims to enhance semantic segmentation performance. Furthermore, the
boundary loss capitalizes on the distinctive features of SGB by directing the
model's attention to the boundary information of the object. Experimental
results on two well-known datasets, namely ISPRS Vaihingen and LoveDA Urban,
demonstrate the effectiveness of our proposed method. The source code for this
work will be accessible at https://github.com/sstary/SSRS.Comment: 10 pages, 4 figure