671 research outputs found
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
Learning Joint Detection, Equalization and Decoding for Short-Packet Communications
We propose and practically demonstrate a joint detection and decoding scheme
for short-packet wireless communications in scenarios that require to first
detect the presence of a message before actually decoding it. For this, we
extend the recently proposed serial Turbo-autoencoder neural network (NN)
architecture and train it to find short messages that can be, all "at once",
detected, synchronized, equalized and decoded when sent over an unsynchronized
channel with memory. The conceptional advantage of the proposed system stems
from a holistic message structure with superimposed pilots for joint detection
and decoding without the need of relying on a dedicated preamble. This results
not only in higher spectral efficiency, but also translates into the
possibility of shorter messages compared to using a dedicated preamble. We
compare the detection error rate (DER), bit error rate (BER) and block error
rate (BLER) performance of the proposed system with a hand-crafted
state-of-the-art conventional baseline and our simulations show a significant
advantage of the proposed autoencoder-based system over the conventional
baseline in every scenario up to messages conveying k = 96 information bits.
Finally, we practically evaluate and confirm the improved performance of the
proposed system over-the-air (OTA) using a software-defined radio (SDR)-based
measurement testbed.Comment: Submitted to IEEE TCO
CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback
To reduce multiuser interference and maximize the spectrum efficiency in
orthogonal frequency division duplexing massive multiple-input multiple-output
(MIMO) systems, the downlink channel state information (CSI) estimated at the
user equipment (UE) is required at the base station (BS). This paper presents a
novel method for massive MIMO CSI feedback via a one-sided one-for-all deep
learning framework. The CSI is compressed via linear projections at the UE, and
is recovered at the BS using deep learning (DL) with plug-and-play priors
(PPP). Instead of using handcrafted regularizers for the wireless channel
responses, the proposed approach, namely CSI-PPPNet, exploits a DL based
denoisor in place of the proximal operator of the prior in an alternating
optimization scheme. In this way, a DL model trained once for denoising can be
repurposed for CSI recovery tasks with arbitrary compression ratio. The
one-sided one-for-all framework reduces model storage space, relieves the
burden of joint model training and model delivery, and could be applied at UEs
with limited device memories and computation power. Extensive experiments over
the open indoor and urban macro scenarios show the effectiveness and advantages
of the proposed method
Massive MIMO systems for 5G: a systematic mapping study on antenna design challenges and channel estimation open issues
The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple-input multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks
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