7 research outputs found
A Multi-Kernel Multi-Code Polar Decoder Architecture
Polar codes have received increasing attention in the past decade, and have
been selected for the next generation of wireless communication standard. Most
research on polar codes has focused on codes constructed from a
polarization matrix, called binary kernel: codes constructed from binary
kernels have code lengths that are bound to powers of . A few recent works
have proposed construction methods based on multiple kernels of different
dimensions, not only binary ones, allowing code lengths different from powers
of . In this work, we design and implement the first multi-kernel successive
cancellation polar code decoder in literature. It can decode any code
constructed with binary and ternary kernels: the architecture, sized for a
maximum code length , is fully flexible in terms of code length, code
rate and kernel sequence. The decoder can achieve frequency of more than
GHz in nm CMOS technology, and a throughput of Mb/s. The area
occupation ranges between mm for and mm for
. Implementation results show an unprecedented degree of
flexibility: with , up to code lengths can be decoded with
the same hardware, along with any kernel sequence and code rate
Implementation of a High-Throughput Fast-SSC Polar Decoder with Sequence Repetition Node
Even though polar codes were adopted in the latest 5G cellular standard, they
still have the fundamental problem of high decoding latency. Aiming at solving
this problem, a fast simplified successive cancellation (Fast-SSC) decoder
based on the new class of sequence repetition (SR) nodes has been proposed
recently in \cite{sr2020} and has a lower required number of time steps than
other existing Fast-SSC decoders in theory. This paper focuses on the hardware
implementation of this SR node-based fast-SSC (SRFSC) decoder. The
implementation results for a polar code with length 1024 and code rate 1/2 show
that our implementation has a throughput of Mbps on an Altera Stratix IV
FPGA, which is 17.9% higher with respect to the previous work.Comment: 6 pages, 6 figures. Accepted and to appear in IEEE International
Workshop on Signal Processing Systems, Oct 2020 (SIPS2020). The latest
version. arXiv admin note: text overlap with arXiv:2005.0439
Wireless End-to-End Image Transmission System using Semantic Communications
Semantic communication is considered the future of mobile communication,
which aims to transmit data beyond Shannon's theorem of communications by
transmitting the semantic meaning of the data rather than the bit-by-bit
reconstruction of the data at the receiver's end. The semantic communication
paradigm aims to bridge the gap of limited bandwidth problems in modern
high-volume multimedia application content transmission. Integrating AI
technologies with the 6G communications networks paved the way to develop
semantic communication-based end-to-end communication systems. In this study,
we have implemented a semantic communication-based end-to-end image
transmission system, and we discuss potential design considerations in
developing semantic communication systems in conjunction with physical channel
characteristics. A Pre-trained GAN network is used at the receiver as the
transmission task to reconstruct the realistic image based on the Semantic
segmented image at the receiver input. The semantic segmentation task at the
transmitter (encoder) and the GAN network at the receiver (decoder) is trained
on a common knowledge base, the COCO-Stuff dataset. The research shows that the
resource gain in the form of bandwidth saving is immense when transmitting the
semantic segmentation map through the physical channel instead of the ground
truth image in contrast to conventional communication systems. Furthermore, the
research studies the effect of physical channel distortions and quantization
noise on semantic communication-based multimedia content transmission.Comment: Accepted for IEEE Acces
Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorial
Ultra-reliable low-latency communication (URLLC) constitutes a key service
class of the fifth generation and beyond cellular networks. Notably, designing
and supporting URLLC poses a herculean task due to the fundamental need to
identify and accurately characterize the underlying statistical models in which
the system operates, e.g., interference statistics, channel conditions, and the
behavior of protocols. In general, multi-layer end-to-end approaches
considering all the potential delay and error sources and proper statistical
tools and methodologies are inevitably required for providing strong
reliability and latency guarantees. This paper contributes to the body of
knowledge in the latter aspect by providing a tutorial on several statistical
tools and methodologies that are useful for designing and analyzing URLLC
systems. Specifically, we overview the frameworks related to i) reliability
theory, ii) short packet communications, iii) inequalities, distribution
bounds, and tail approximations, iv) rare events simulation, vi) queuing theory
and information freshness, and v) large-scale tools such as stochastic
geometry, clustering, compressed sensing, and mean-field games. Moreover, we
often refer to prominent data-driven algorithms within the scope of the
discussed tools/methodologies. Throughout the paper, we briefly review the
state-of-the-art works using the addressed tools and methodologies, and their
link to URLLC systems. Moreover, we discuss novel application examples focused
on physical and medium access control layers. Finally, key research challenges
and directions are highlighted to elucidate how URLLC analysis/design research
may evolve in the coming years.Comment: Accepted in IEEE Proceedings of the IEEE. 40 pages, 20 figures, 11
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