Range-Division Multiplexing for MIMO OFDM Joint Radar and Communications

Lang, Oliver; Hofbauer, Christian; Feger, Reinhard; Huemer, Mario

doi:10.1109/tvt.2022.3203205

Cited by 11 publications

(24 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cyclic prefix (CP)-OFDM (simply referred to as OFDM) is widely adopted in wireless communications [16] and the reader is thus expected to be familiar with it. This section briefly recaps the OFDM signal model in complex baseband from [8], [15]. The derivation of this signal model considers only a single Rx antenna, while a possible extension to multiple Rx antennas can be easily adapted.…”

Section: Radar Signal Modelmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Doppler-Division Multiplexing on OFDM Joint Sensing and Communication Systems

Lang,

Hofbauer,

Feger

et al. 2024

IEEE Open J. Signal Process.

Self Cite

View full text Add to dashboard Cite

A promising waveform candidate for future joint sensing and communication systems is orthogonal frequency-division multiplexing (OFDM). For such systems, supporting multiple transmit antennas requires multiplexing methods for the generation of orthogonal transmit signals, where equidistant subcarrier interleaving (ESI) is the most popular multiplexing method. In this work, we analyze a multiplexing method called Doppler-division multiplexing (DDM). This method applies a phase shift from OFDM symbol to OFDM symbol to separate signals transmitted by different Tx antennas along the velocity axis of the range-Doppler map. The main focus of this work lies on the implications of DDM on the communication task. It will be shown that for DDM, the channels observed in the communication receiver are heavily time-varying, preventing any meaningful transmission of data when not taken into account. In this work, a communication system designed to combat these time-varying channels is proposed, which includes methods for data estimation, synchronization, and channel estimation. Bit error ratio (BER) simulations demonstrate the superiority of this communications system compared to ESI-based systems.

show abstract

Section: Radar Signal Modelmentioning

confidence: 99%

“…The multiplexing method analyzed in [15] is referred to as range-division multiplexing (RDMult), and it applies a phase shift from subcarrier to subcarrier to shift the signal components of the RDM along the range axis.…”

mentioning

confidence: 99%

Effects of Doppler-Division Multiplexing on OFDM Joint Sensing and Communication Systems

Lang,

Hofbauer,

Feger

et al. 2024

IEEE Open J. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Relaying techniques, at the heart of modern communication systems, are instrumental in overcoming signal propagation challenges and enhancing data transmission performance [12][13][14][15]. The usage of relay selection, involves the strategic choice of relay nodes to assist in data transmission, significantly impacting data rates and overall system performance [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

IoT and Relaying Aided Transmission Technologies for Monitoring Electrical Equipment Status

Yi,

Chen,

Chen

et al. 2024

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

This paper presents a novel approach to monitoring the status of electrical equipment using Internet of Things (IoT) and relaying-aided transmission technologies, where data rate is used as a key metric for evaluating system monitoring performance. In this framework, relaying plays a pivotal role, enhancing the robustness and efficiency of data transmission in the monitoring process. We employ the optimal relay selection algorithms to identify and employ the most effective relay to assist in the transmission, thereby optimizing the communication link between the electrical equipment and the monitoring system. To provide a comprehensive understanding of the system's capabilities, we delve into the analytical aspects by deriving expressions for the data rate. These expressions offer insights into the theoretical performance limits and the factors influencing the efficiency of the system. The theoretical framework is further complemented by a series of simulations. These simulations validate the analytical models developed in the study, and provide practical scenarios to demonstrate the real-world applicability and effectiveness of the proposed IoT and relaying-aided transmission technologies in monitoring electrical equipment.

show abstract

“…The inception of cellular networks and their evolution, as chronicled, facilitated ubiquitous connectivity and paved the way for mobile computing paradigms. Wireless mesh networks (WMNs) and their architectural nuances were expounded in [7][8][9], underscoring their significance in forming Ad Hoc networks for data dissemination. The role of wireless transmission in Internet of Things (IoT) was underscored, exemplifying how wireless connections bind together the fabric of interconnected devices, enabling data exchange.…”

Section: Introductionmentioning

confidence: 99%

Compression and Transmission of Big AI Model Based on Deep Learning

Lin,

Zhou,

Yang

et al. 2023

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

In recent years, big AI models have demonstrated remarkable performance in various artificial intelligence (AI) tasks. However, their widespread use has introduced significant challenges in terms of model transmission and training. This paper addresses these challenges by proposing a solution that involves the compression and transmission of large models using deep learning techniques, thereby ensuring the efficiency of model training. To achieve this objective, we leverage deep convolutional networks to design a novel approach for compressing and transmitting large models. Specifically, deep convolutional networks are employed for model compression, providing an effective means to reduce the size of large models without compromising their representational capacity. The proposed framework also includes carefully devised encoding and decoding strategies to guarantee the restoration of model integrity after transmission. Furthermore, a tailored loss function is designed for model training, facilitating the optimization of both the transmission and training performance within the system. Through experimental evaluation, we demonstrate the efficacy of the proposed approach in addressing the challenges associated with large model transmission and training. The results showcase the successful compression and subsequent accurate reconstruction of large models, while maintaining their performance across various AI tasks. This work contributes to the ongoing research in enhancing the practicality and efficiency of deploying large models in real-world AI applications.

show abstract

Range-Division Multiplexing for MIMO OFDM Joint Radar and Communications

Cited by 11 publications

References 47 publications

Effects of Doppler-Division Multiplexing on OFDM Joint Sensing and Communication Systems

Effects of Doppler-Division Multiplexing on OFDM Joint Sensing and Communication Systems

IoT and Relaying Aided Transmission Technologies for Monitoring Electrical Equipment Status

Compression and Transmission of Big AI Model Based on Deep Learning

Contact Info

Product

Resources

About