Feedforward multiple access satellite communications

Laufer, S.; Snyders, J.

doi:10.1109/49.144886

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, existing conventional communication was mainly developed with far-field communication theories and techniques, but near-field communication is essential in the future system since the number of antennas has increased incredibly, and the Rayleigh distance in the radius of a typical 5G cell has increased [15]. As a result, spherical waves should be exploited to realize near-field beam-focusing in XL-MIMO systems to focus signals on a specific location, rather than the conventional far-field beam-steering that steers signals towards a specific angle [16], [17]. In addition, it has been revealed that there is potential in the beam-focusing method in the near-field [9], [10], which is the advanced version of traditional beamforming based on the linearly functional phase in the array response.…”

Section: Referencementioning

confidence: 99%

An NN-Aided Near-and-Far-Field Classifier via Channel Hankelization in XL-MIMO Systems

Kim,

Ozger

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Compared to classical communication systems, sixth-generation (6G) communication requires higher data rates, lower latency, improved energy efficiency, and more diverse users. To satisfy these many requirements, the extremely large-scale massive multiple-input multiple-output (XL-MIMO) system is attracting attention as a promising technology in 6G communication. Depending on the distance between a transmitter and a receiver, the electromagnetic radiation channels in XL-MIMO systems are divided into two models: near-field and far-field channels. The main difference between far-field and nearfield is the phase-linearity, resulting in a need for a differentiated system design such as beam management. As a consequence, it is essential to classify near-field and far-field. This paper presents a new neural network (NN)-aided framework for classifying near-field and far-field using the partially captured channel in downlink scenarios in XL-MIMO systems. It is based on the mathematical reasoning that an effective latent space can be constructed with a small amount of data by using the singular values of the channel Hankelization. Briefly, it is to determine the one-hot encoding vector corresponding to each field and learn the singular values of the Hankelized channel matrix. It is noteworthy that this framework operates using the short length of input vectors and the small size of the training dataset. Simulation results show that the proposed method shows the detection rate of about 90% in almost all scenarios. Interestingly, the proposed method shows almost 100% of detection ratio in high SNR environments. It is believed that the proposed method shows superior performance than naïve approaches in various environments, discovering the suitable domain to classify near-field and far-field channels.INDEX TERMS Extremely large-scale massive MIMO, near-field channel, far-field channel, Hankelization, neural networks, binary classification.

show abstract