105 research outputs found
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G
The next wave of wireless technologies is proliferating in connecting things
among themselves as well as to humans. In the era of the Internet of things
(IoT), billions of sensors, machines, vehicles, drones, and robots will be
connected, making the world around us smarter. The IoT will encompass devices
that must wirelessly communicate a diverse set of data gathered from the
environment for myriad new applications. The ultimate goal is to extract
insights from this data and develop solutions that improve quality of life and
generate new revenue. Providing large-scale, long-lasting, reliable, and near
real-time connectivity is the major challenge in enabling a smart connected
world. This paper provides a comprehensive survey on existing and emerging
communication solutions for serving IoT applications in the context of
cellular, wide-area, as well as non-terrestrial networks. Specifically,
wireless technology enhancements for providing IoT access in fifth-generation
(5G) and beyond cellular networks, and communication networks over the
unlicensed spectrum are presented. Aligned with the main key performance
indicators of 5G and beyond 5G networks, we investigate solutions and standards
that enable energy efficiency, reliability, low latency, and scalability
(connection density) of current and future IoT networks. The solutions include
grant-free access and channel coding for short-packet communications,
non-orthogonal multiple access, and on-device intelligence. Further, a vision
of new paradigm shifts in communication networks in the 2030s is provided, and
the integration of the associated new technologies like artificial
intelligence, non-terrestrial networks, and new spectra is elaborated. Finally,
future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&
Compression Ratio Learning and Semantic Communications for Video Imaging
Camera sensors have been widely used in intelligent robotic systems.
Developing camera sensors with high sensing efficiency has always been
important to reduce the power, memory, and other related resources. Inspired by
recent success on programmable sensors and deep optic methods, we design a
novel video compressed sensing system with spatially-variant compression
ratios, which achieves higher imaging quality than the existing snapshot
compressed imaging methods with the same sensing costs. In this article, we
also investigate the data transmission methods for programmable sensors, where
the performance of communication systems is evaluated by the reconstructed
images or videos rather than the transmission of sensor data itself. Usually,
different reconstruction algorithms are designed for applications in high
dynamic range imaging, video compressive sensing, or motion debluring. This
task-aware property inspires a semantic communication framework for
programmable sensors. In this work, a policy-gradient based reinforcement
learning method is introduced to achieve the explicit trade-off between the
compression (or transmission) rate and the image distortion. Numerical results
show the superiority of the proposed methods over existing baselines
Optimization and Applications of Modern Wireless Networks and Symmetry
Due to the future demands of wireless communications, this book focuses on channel coding, multi-access, network protocol, and the related techniques for IoT/5G. Channel coding is widely used to enhance reliability and spectral efficiency. In particular, low-density parity check (LDPC) codes and polar codes are optimized for next wireless standard. Moreover, advanced network protocol is developed to improve wireless throughput. This invokes a great deal of attention on modern communications
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
We propose a general framework for decoding quantum error-correcting codes
with generative modeling. The model utilizes autoregressive neural networks,
specifically Transformers, to learn the joint probability of logical operators
and syndromes. This training is in an unsupervised way, without the need for
labeled training data, and is thus referred to as pre-training. After the
pre-training, the model can efficiently compute the likelihood of logical
operators for any given syndrome, using maximum likelihood decoding. It can
directly generate the most-likely logical operators with computational
complexity in the number of logical qubits , which is
significantly better than the conventional maximum likelihood decoding
algorithms that require computation. Based on the pre-trained
model, we further propose refinement to achieve more accurately the likelihood
of logical operators for a given syndrome by directly sampling the stabilizer
operators. We perform numerical experiments on stabilizer codes with small code
distances, using both depolarizing error models and error models with
correlated noise. The results show that our approach provides significantly
better decoding accuracy than the minimum weight perfect matching and
belief-propagation-based algorithms. Our framework is general and can be
applied to any error model and quantum codes with different topologies such as
surface codes and quantum LDPC codes. Furthermore, it leverages the
parallelization capabilities of GPUs, enabling simultaneous decoding of a large
number of syndromes. Our approach sheds light on the efficient and accurate
decoding of quantum error-correcting codes using generative artificial
intelligence and modern computational power.Comment: Comments are welcom
When Machine Learning Meets Information Theory: Some Practical Applications to Data Storage
Machine learning and information theory are closely inter-related areas. In this dissertation,
we explore topics in their intersection with some practical applications to data storage.
Firstly, we explore how machine learning techniques can be used to improve data reliability
in non-volatile memories (NVMs). NVMs, such as flash memories, store large volumes of data.
However, as devices scale down towards small feature sizes, they suffer from various kinds of noise and disturbances, thus significantly reducing their reliability. This dissertation explores machine learning techniques to design decoders that make use of natural redundancy (NR) in data for error correction. By NR, we mean redundancy inherent in data, which is not added artificially for error correction. This work studies two different schemes for NR-based error-correcting decoders. In the first scheme, the NR-based decoding algorithm is aware of the data representation scheme (e.g., compression, mapping of symbols to bits, meta-data, etc.), and uses that information for error correction. In the second scenario, the NR-decoder is oblivious of the representation scheme and uses deep neural networks (DNNs) to recognize the file type as well as perform soft decoding on it based on NR. In both cases, these NR-based decoders can be combined with traditional error correction codes (ECCs) to substantially improve their performance.
Secondly, we use concepts from ECCs for designing robust DNNs in hardware. Non-volatile
memory devices like memristors and phase-change memories are used to store the weights of
hardware implemented DNNs. Errors and faults in these devices (e.g., random noise, stuck-at
faults, cell-level drifting etc.) might degrade the performance of such DNNs in hardware. We use
concepts from analog error-correcting codes to protect the weights of noisy neural networks and to design robust neural networks in hardware.
To summarize, this dissertation explores two important directions in the intersection of information theory and machine learning. We explore how machine learning techniques can be useful in improving the performance of ECCs. Conversely, we show how information-theoretic concepts can be used to design robust neural networks in hardware
Artificial Intelligence Aided Receiver Design for Wireless Communication Systems
Physical layer (PHY) design in the wireless communication field realizes gratifying achievements in the past few decades, especially in the emerging cellular communication systems starting from the first generation to the fifth generation (5G). With the gradual increase in technical requirements of large data processing and end-to-end system optimization, introducing artificial intelligence (AI) in PHY design has cautiously become a trend. A deep neural network (DNN), one of the population techniques of AI, enables the utilization of its ‘learnable’ feature to handle big data and establish a global system model. In this thesis, we exploited this characteristic of DNN as powerful assistance to implement two receiver designs in two different use-cases. We considered a DNN-based joint baseband demodulator and channel decoder (DeModCoder), and a DNN-based joint equalizer, baseband demodulator, and channel decoder (DeTecModCoder) in two single operational blocks, respectively. The multi-label classification (MLC) scheme was equipped to the output of conducted DNN model and hence yielded lower computational complexity than the multiple output classification (MOC) manner. The functional DNN model can be trained offline over a wide range of SNR values under different types of noises, channel fading, etc., and deployed in the real-time application; therefore, the demands of estimation of noise variance and statistical information of underlying noise can be avoided. The simulation performances indicated that compared to the corresponding conventional receiver signal processing schemes, the proposed AI-aided receiver designs have achieved the same bit error rate (BER) with around 3 dB lower SNR
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