132,956 research outputs found
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.Comment: Submitted a possible publication to IEEE Wireless Communications
Magazin
From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer
Most research studies on deep learning (DL) applied to the physical layer of
wireless communication do not put forward the critical role of the
accuracy-generalization trade-off in developing and evaluating practical
algorithms. To highlight the disadvantage of this common practice, we revisit a
data decoding example from one of the first papers introducing DL-based
end-to-end wireless communication systems to the research community and
promoting the use of artificial intelligence (AI)/DL for the wireless physical
layer. We then put forward two key trade-offs in designing DL models for
communication, namely, accuracy versus generalization and compression versus
latency. We discuss their relevance in the context of wireless communications
use cases using emerging DL models including large language models (LLMs).
Finally, we summarize our proposed evaluation guidelines to enhance the
research impact of DL on wireless communications. These guidelines are an
attempt to reconcile the empirical nature of DL research with the rigorous
requirement metrics of wireless communications systems
Security and Privacy for Modern Wireless Communication Systems
The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks
EsaNet: Environment Semantics Enabled Physical Layer Authentication
Wireless networks are vulnerable to physical layer spoofing attacks due to
the wireless broadcast nature, thus, integrating communications and security
(ICAS) is urgently needed for 6G endogenous security. In this letter, we
propose an environment semantics enabled physical layer authentication network
based on deep learning, namely EsaNet, to authenticate the spoofing from the
underlying wireless protocol. Specifically, the frequency independent wireless
channel fingerprint (FiFP) is extracted from the channel state information
(CSI) of a massive multi-input multi-output (MIMO) system based on environment
semantics knowledge. Then, we transform the received signal into a
two-dimensional red green blue (RGB) image and apply the you only look once
(YOLO), a single-stage object detection network, to quickly capture the FiFP.
Next, a lightweight classification network is designed to distinguish the
legitimate from the illegitimate users. Finally, the experimental results show
that the proposed EsaNet can effectively detect physical layer spoofing attacks
and is robust in time-varying wireless environments
Deep Learning Enabled Semantic Communication Systems
Recently, deep learned enabled end-to-end (E2E) communication systems have
been developed to merge all physical layer blocks in the traditional
communication systems, which make joint transceiver optimization possible.
Powered by deep learning, natural language processing (NLP) has achieved great
success in analyzing and understanding large amounts of language texts.
Inspired by research results in both areas, we aim to providing a new view on
communication systems from the semantic level. Particularly, we propose a deep
learning based semantic communication system, named DeepSC, for text
transmission. Based on the Transformer, the DeepSC aims at maximizing the
system capacity and minimizing the semantic errors by recovering the meaning of
sentences, rather than bit- or symbol-errors in traditional communications.
Moreover, transfer learning is used to ensure the DeepSC applicable to
different communication environments and to accelerate the model training
process. To justify the performance of semantic communications accurately, we
also initialize a new metric, named sentence similarity. Compared with the
traditional communication system without considering semantic information
exchange, the proposed DeepSC is more robust to channel variation and is able
to achieve better performance, especially in the low signal-to-noise (SNR)
regime, as demonstrated by the extensive simulation results.Comment: 13 pages, Journal, accepted by IEEE TS
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