85 research outputs found
A Universal Receiver for Uplink NOMA Systems
Given its capability in efficient radio resource sharing, non-orthogonal
multiple access (NOMA) has been identified as a promising technology in 5G to
improve the system capacity, user connectivity, and scheduling latency. A dozen
of uplink NOMA schemes have been proposed recently and this paper considers the
design of a universal receiver suitable for all potential designs of NOMA
schemes. Firstly, a general turbo-like iterative receiver structure is
introduced, under which, a universal expectation propagation algorithm (EPA)
detector with hybrid parallel interference cancellation (PIC) is proposed (EPA
in short). Link-level simulations show that the proposed EPA receiver can
achieve superior block error rate (BLER) performance with implementation
friendly complexity and fast convergence, and is always better than the
traditional codeword level MMSE-PIC receiver for various kinds of NOMA schemes.Comment: This paper has been accepted by IEEE/CIC International Conference on
Communications in China (ICCC 2018). 5 pages, 4 figure
Turbo-like Iterative Multi-user Receiver Design for 5G Non-orthogonal Multiple Access
Non-orthogonal multiple access (NoMA) as an efficient way of radio resource
sharing has been identified as a promising technology in 5G to help improving
system capacity, user connectivity, and service latency in 5G communications.
This paper provides a brief overview of the progress of NoMA transceiver study
in 3GPP, with special focus on the design of turbo-like iterative multi-user
(MU) receivers. There are various types of MU receivers depending on the
combinations of MU detectors and interference cancellation (IC) schemes.
Link-level simulations show that expectation propagation algorithm (EPA) with
hybrid parallel interference cancellation (PIC) is a promising MU receiver,
which can achieve fast convergence and similar performance as message passing
algorithm (MPA) with much lower complexity.Comment: Accepted by IEEE 88th Vehicular Technology Conference (IEEE VTC-2018
Fall), 5 pages, 6 figure
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
We consider the ubiquitous linear inverse problems with additive Gaussian
noise and propose an unsupervised general-purpose sampling approach called
diffusion model based posterior sampling (DMPS) to reconstruct the unknown
signal from noisy linear measurements. Specifically, the prior of the unknown
signal is implicitly modeled by one pre-trained diffusion model (DM). In
posterior sampling, to address the intractability of exact noise-perturbed
likelihood score, a simple yet effective noise-perturbed pseudo-likelihood
score is introduced under the uninformative prior assumption. While DMPS
applies to any kind of DM with proper modifications, we focus on the ablated
diffusion model (ADM) as one specific example and evaluate its efficacy on a
variety of linear inverse problems such as image super-resolution, denoising,
deblurring, colorization. Experimental results demonstrate that, for both
in-distribution and out-of-distribution samples, DMPS achieves highly
competitive or even better performances on various tasks while being 3 times
faster than the leading competitor. The code to reproduce the results is
available at https://github.com/mengxiangming/dmps.Comment: 20 pages. The code is available at
https://github.com/mengxiangming/dmp
Quantized Compressed Sensing with Score-Based Generative Models
We consider the general problem of recovering a high-dimensional signal from
noisy quantized measurements. Quantization, especially coarse quantization such
as 1-bit sign measurements, leads to severe information loss and thus a good
prior knowledge of the unknown signal is helpful for accurate recovery.
Motivated by the power of score-based generative models (SGM, also known as
diffusion models) in capturing the rich structure of natural signals beyond
simple sparsity, we propose an unsupervised data-driven approach called
quantized compressed sensing with SGM (QCS-SGM), where the prior distribution
is modeled by a pre-trained SGM. To perform posterior sampling, an annealed
pseudo-likelihood score called {\textit{noise perturbed pseudo-likelihood
score}} is introduced and combined with the prior score of SGM. The proposed
QCS-SGM applies to an arbitrary number of quantization bits. Experiments on a
variety of baseline datasets demonstrate that the proposed QCS-SGM
significantly outperforms existing state-of-the-art algorithms by a large
margin for both in-distribution and out-of-distribution samples. Moreover, as a
posterior sampling method, QCS-SGM can be easily used to obtain confidence
intervals or uncertainty estimates of the reconstructed results. The code is
available at https://github.com/mengxiangming/QCS-SGM.Comment: 29 pages, code available at https://github.com/mengxiangming/QCS-SG
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