Deep Learning Based Resource Assignment for Wireless Networks

Kim, Minseok; Lee, Hoon; Lee, Hongju; Lee, Inkyu

doi:10.1109/lcomm.2021.3116233

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the performance of existing wireless technologies, several researchers have tried to apply deep-learning networks to physical (PHY) layer [19], channel estimation [20], modulation classification [21,22], resource allocation [23], beamforming [24], power control [25], and spectrum sensing [26][27][28][29][30][31]. The work [19] designed a communication system as an end-to-end reconstruction task that jointly optimizes the components of a transmitter and receiver.…”

Section: Related Workmentioning

confidence: 99%

“…In [20], they proposed a deep learning framework to improve channel estimation and DOA estimation. The works [23,25] utilized deep learning to improve existing resource management technologies such as frequency resource allocation and power allocation, and the authors of [24] proposed a deep learning framework that designs beamforming vectors for a MISO system. For spectrum sensing, the works [26][27][28][29][30] proposed supervised learning-based schemes to decide whether a channel is occupied by a primary user, while the authors of [31] developed an unsupervised deep learning based spectrum sensing method.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Impact Analysis of Emerging Semantic Communication Systems on Network Performance

2022

View full text Add to dashboard Cite

With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication focuses on the successful delivery of transmitted messages such as human language communication. In order to realize such new communication, both transmitter and receiver should share the same background knowledge with each other. Recently, several researchers have developed task-specific SC systems by exploiting astonishing achievements in deep learning, which can allow the same knowledge to be shared between them. However, since such SC systems are specialized to handle specific applications, not all users can be serviced by the SC systems. Therefore, a network will face a coexistence of an SC system and a traditional communication (TC) system. In this paper, we investigate how introducing emerging SC systems affects the performance of the TC system from a network perspective. For analysis, we consider the signal-to-noise ratio (SNR) differently for the user served by an SC system and the user served by a TC system. Then, by using two different SNR equations, we formulate a max-min fairness problem in the coexistence of SC and TC systems. Via extensive numerical results, we compare the network performance of TC and SC users with and without SC systems, and then confirm that SC systems are indeed a promising next-generation communication alternative.

show abstract