Optimizing Reconfigurable Intelligent Surfaces for Short Transmissions: How Detailed Configurations Can Be Afforded?

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This paper examines how to minimize the energy consumption of a user equipment (UE) when transmitting short data payloads. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS), which requires additional pilot signals to be configured, to improve the channel conditions. The challenge is that the pilot signals increase the energy consumption and must be balanced against energy savings during data transmission. We derive a formula for the energy consumption, including both pilot and data transmission powers and the effects of imperfect channel state information and discrete phase-shifts. To shorten the pilot length, we propose dividing the RIS into subarrays of multiple elements using the same reflection coefficient. The pilot power and subarray size are tuned to the payload length to minimize the energy consumption. Analytical results show that there exists a unique energy-minimizing solution. For small payloads and when the direct path loss between the BS and UE is weak compared to the path loss via the RIS, the solution is using subarrays with many elements and low pilot power and vice versa. The optimal percentage of energy spent on pilot signaling is in the order of 10-40%. IEEE

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6G, Array signal processing, discrete phase-shifts, Energy consumption, energy efficiency, Finite element analysis, Optimization, Payloads, phase-shift optimization, Reconfigurable intelligent surface, Signal to noise ratio, subarrays, Wireless communication, Array processing, Channel state information, Data transfer, Energy utilization, Finite element method, Signal to noise ratio, 6g, Array signal processing, Discrete phase, Discrete phase-shift, Energy-consumption, Finite element analyse, Optimisations, Payload, Phase-shift optimization, Reconfigurable, Reconfigurable intelligent surface, Subarray, Wireless communications, Energy efficiency, Optimization, Energy utilization, Reconfigurable, Signal Processing (eess.SP), FOS: Computer and information sciences, Finite element method, discrete phase-shifts, Discrete phase, Wireless communications, Computer Science - Information Theory, Wireless communication, Finite element analyse, Discrete phase-shift, phase-shift optimization, Reconfigurable intelligent surface, Array processing, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Energy-consumption, energy efficiency, 6G, Signal to noise ratio, Payload, Payloads, Information Theory (cs.IT), Finite element analysis, subarrays, Data transfer, Energy consumption, Subarray, Energy efficiency, Array signal processing, Channel state information, Optimisations, 6g, Phase-shift optimization

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02 engineering and technology, 0202 electrical engineering, electronic engineering, information engineering

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