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In a cellular wireless system, numerous users can connect simultaneously and/or on the same frequency. The more effectively frequency and time resources are reused, the greater the network size, assuming reliable detection of transmitted signals. Operators can be detached by frequency, time, or code. The spatial dimension in multiple-input multiple-output (MIMO) channels adds an extra layer to distinct users, enabling more efficient reprocessing of frequency and time possessions, thereby boosting network capacity. Optical beamforming emerges as a method to enhance capacity. It offers benefits such as minimal insertion losses and broad bandwidth transmission, positioning it as a promising technology for future mm-wave 5G/6G networks. This chapter systematically evaluates various optical signal processor beamforming network (OSPBFN) architectures utilizing true time delays, photonic fiber, mirrors, and optical phase shifters. In the proposed OSPBFN, the look directions (LD) and null directions (ND) are the inputs to the ML algorithm, which converts the input to the voltage. In turn, the mirror attached to the comb-drive electrostatic actuator (CDEA) controls the phases and amplitudes of the optical signal, which are radiated after down conversion. The ML method is implemented using Matlab code. The OSPBFN is thoroughly characterized using CoventorWare software.

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Optical Signal Processing Beamforming Network for 6G Mobile Communication

  • 章节

  • 发表日期：

    2025年

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  • B. Elizabeth Caroline,
  • Susan Christiana,
  • D. Sathish Kumar,
  • J. Vidhya,
  • K. Sagadevan,
  • M. Margarat

摘要

In a cellular wireless system, numerous users can connect simultaneously and/or on the same frequency. The more effectively frequency and time resources are reused, the greater the network size, assuming reliable detection of transmitted signals. Operators can be detached by frequency, time, or code. The spatial dimension in multiple-input multiple-output (MIMO) channels adds an extra layer to distinct users, enabling more efficient reprocessing of frequency and time possessions, thereby boosting network capacity. Optical beamforming emerges as a method to enhance capacity. It offers benefits such as minimal insertion losses and broad bandwidth transmission, positioning it as a promising technology for future mm-wave 5G/6G networks. This chapter systematically evaluates various optical signal processor beamforming network (OSPBFN) architectures utilizing true time delays, photonic fiber, mirrors, and optical phase shifters. In the proposed OSPBFN, the look directions (LD) and null directions (ND) are the inputs to the ML algorithm, which converts the input to the voltage. In turn, the mirror attached to the comb-drive electrostatic actuator (CDEA) controls the phases and amplitudes of the optical signal, which are radiated after down conversion. The ML method is implemented using Matlab code. The OSPBFN is thoroughly characterized using CoventorWare software.