181 research outputs found
Message Passing in C-RAN: Joint User Activity and Signal Detection
In cloud radio access network (C-RAN), remote radio heads (RRHs) and users
are uniformly distributed in a large area such that the channel matrix can be
considered as sparse. Based on this phenomenon, RRHs only need to detect the
relatively strong signals from nearby users and ignore the weak signals from
far users, which is helpful to develop low-complexity detection algorithms
without causing much performance loss. However, before detection, RRHs require
to obtain the realtime user activity information by the dynamic grant
procedure, which causes the enormous latency. To address this issue, in this
paper, we consider a grant-free C-RAN system and propose a low-complexity
Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified
channel, which jointly detects the user activity and signal. Since active users
are assumed to transmit Gaussian signals at any time, the user activity can be
regarded as a Bernoulli variable and the signals from all users obey a
Bernoulli-Gaussian distribution. In the BGMP, the detection functions for
signals are designed with respect to the Bernoulli-Gaussian variable. Numerical
results demonstrate the robustness and effectivity of the BGMP. That is, for
different sparsified channels, the BGMP can approach the mean-square error
(MSE) of the genie-aided sparse minimum mean-square error (GA-SMMSE) which
exactly knows the user activity information. Meanwhile, the fast convergence
and strong recovery capability for user activity of the BGMP are also verified.Comment: Conference, 6 pages, 7 figures, accepted by IEEE Globecom 201
Low-Complexity and Information-Theoretic Optimal Memory AMP for Coded Generalized MIMO
This paper considers a generalized multiple-input multiple-output (GMIMO)
with practical assumptions, such as massive antennas, practical channel coding,
arbitrary input distributions, and general right-unitarily-invariant channel
matrices (covering Rayleigh fading, certain ill-conditioned and correlated
channel matrices). Orthogonal/vector approximate message passing (OAMP/VAMP)
has been proved to be information-theoretically optimal in GMIMO, but it is
limited to high complexity. Meanwhile, low-complexity memory approximate
message passing (MAMP) was shown to be Bayes optimal in GMIMO, but channel
coding was ignored. Therefore, how to design a low-complexity and
information-theoretic optimal receiver for GMIMO is still an open issue. In
this paper, we propose an information-theoretic optimal MAMP receiver for coded
GMIMO, whose achievable rate analysis and optimal coding principle are provided
to demonstrate its information-theoretic optimality. Specifically, state
evolution (SE) for MAMP is intricately multi-dimensional because of the nature
of local memory detection. To this end, a fixed-point consistency lemma is
proposed to derive the simplified variational SE (VSE) for MAMP, based on which
the achievable rate of MAMP is calculated, and the optimal coding principle is
derived to maximize the achievable rate. Subsequently, we prove the
information-theoretic optimality of MAMP. Numerical results show that the
finite-length performances of MAMP with optimized LDPC codes are about 1.0 -
2.7 dB away from the associated constrained capacities. It is worth noting that
MAMP can achieve the same performance as OAMP/VAMP with 0.4% of the time
consumption for large-scale systems.Comment: 6 pages, 6 figures, accepted at GLOBECOM 202
Capacity-Achieving MIMO-NOMA: Iterative LMMSE Detection
This paper considers a low-complexity iterative Linear Minimum Mean Square
Error (LMMSE) multi-user detector for the Multiple-Input and Multiple-Output
system with Non-Orthogonal Multiple Access (MIMO-NOMA), where multiple
single-antenna users simultaneously communicate with a multiple-antenna base
station (BS). While LMMSE being a linear detector has a low complexity, it has
suboptimal performance in multi-user detection scenario due to the mismatch
between LMMSE detection and multi-user decoding. Therefore, in this paper, we
provide the matching conditions between the detector and decoders for
MIMO-NOMA, which are then used to derive the achievable rate of the iterative
detection. We prove that a matched iterative LMMSE detector can achieve (i) the
optimal capacity of symmetric MIMO-NOMA with any number of users, (ii) the
optimal sum capacity of asymmetric MIMO-NOMA with any number of users, (iii)
all the maximal extreme points in the capacity region of asymmetric MIMO-NOMA
with any number of users, (iv) all points in the capacity region of two-user
and three-user asymmetric MIMO-NOMA systems. In addition, a kind of practical
low-complexity error-correcting multiuser code, called irregular
repeat-accumulate code, is designed to match the LMMSE detector. Numerical
results shows that the bit error rate performance of the proposed iterative
LMMSE detection outperforms the state-of-art methods and is within 0.8dB from
the associated capacity limit.Comment: Accepted by IEEE TSP, 16 pages, 9 figures. This is the first work
that proves the low-complexity iterative receiver (Parallel Interference
Cancellation) can achieve the capacity of multi-user MIMO systems. arXiv
admin note: text overlap with arXiv:1604.0831
Neural Preset for Color Style Transfer
In this paper, we present a Neural Preset technique to address the
limitations of existing color style transfer methods, including visual
artifacts, vast memory requirement, and slow style switching speed. Our method
is based on two core designs. First, we propose Deterministic Neural Color
Mapping (DNCM) to consistently operate on each pixel via an image-adaptive
color mapping matrix, avoiding artifacts and supporting high-resolution inputs
with a small memory footprint. Second, we develop a two-stage pipeline by
dividing the task into color normalization and stylization, which allows
efficient style switching by extracting color styles as presets and reusing
them on normalized input images. Due to the unavailability of pairwise
datasets, we describe how to train Neural Preset via a self-supervised
strategy. Various advantages of Neural Preset over existing methods are
demonstrated through comprehensive evaluations. Notably, Neural Preset enables
stable 4K color style transfer in real-time without artifacts. Besides, we show
that our trained model can naturally support multiple applications without
fine-tuning, including low-light image enhancement, underwater image
correction, image dehazing, and image harmonization. Project page with demos:
https://zhkkke.github.io/NeuralPreset .Comment: Project page with demos: https://zhkkke.github.io/NeuralPreset .
Artifact-free real-time 4K color style transfer via AI-generated presets.
CVPR 202
Service Now: CMDB Research
The MAPFRE Capstone team has been tasked with reviewing and recommending roadmap on the existing CMDB configuration. Paper discusses the team’s overall research on ServiceNow CMDB, Client’s deliverables and introduction to the latest technological innovations. Based on given objectives and team’s analysis we have recommended key solutions for the client to better understand the IT environment areas of business service impact, asset management, compliance, and configuration management. In addition, our research has covered all the majority of the technical and functional areas to provide greater visibility and insight into existing CMDB and IT environment
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