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An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells.

ABSTRACT: As 5G small cells gradually become the main force of 5G indoor deployment, it is necessary to study channel estimators for 5G small base stations, but there has been limited research on high-performance channel estimators in recent years. This study implemented a low-delay, low-overhead, relatively universal channel estimation module by dedicated instruction set acceleration including reference signal estimation, Wiener, 1st order, and 2nd order interpolations in frequency and time domains. The instruction level acceleration is on our vector processor, yet is suitable for other commercial and academic vector processors. Through instruction acceleration, compared with the existing general vector processing instruction sets, the processor performance of the LS estimation module and Wiener filter interpolation in the frequency domain is improved by 50% and 37.5%, respectively. The BER VS SNR measure of time-frequency Wiener filter interpolation achieves 4 db compared with linear interpolation, meaning our instruction level acceleration can be an optimum solution.

SUBMITTER: Long B

PROVIDER: S-EPMC8838477 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells.

Long Biao B Liu Dake D Sun Yipeng Y

Sensors (Basel, Switzerland) 20220119 3

As 5G small cells gradually become the main force of 5G indoor deployment, it is necessary to study channel estimators for 5G small base stations, but there has been limited research on high-performance channel estimators in recent years. This study implemented a low-delay, low-overhead, relatively universal channel estimation module by dedicated instruction set acceleration including reference signal estimation, Wiener, 1st order, and 2nd order interpolations in frequency and time domains. The ...[more]

PMID: 35161499

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Project description:We investigate fundamental decisions in the design of instruction set architectures for linear genetic programs that are used as both model systems in evolutionary biology and underlying solution representations in evolutionary computation. We subjected digital organisms with each tested architecture to seven different computational environments designed to present a range of evolutionary challenges. Our goal was to engineer a general purpose architecture that would be effective under a broad range of evolutionary conditions. We evaluated six different types of architectural features for the virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more precisely modify the function of genetic instructions, (2) memory: we provided an increased number of registers in the virtual CPUs, (3) decoupled sensors and actuators: we separated input and output operations to enable greater control over data flow. We also tested a variety of methods to regulate expression: (4) explicit labels that allow programs to dynamically refer to specific genome positions, (5) position-relative search instructions, and (6) multiple new flow control instructions, including conditionals and jumps. Each of these features also adds complication to the instruction set and risks slowing evolution due to epistatic interactions. Two features (multiple argument specification and separated I/O) demonstrated substantial improvements in the majority of test environments, along with versions of each of the remaining architecture modifications that show significant improvements in multiple environments. However, some tested modifications were detrimental, though most exhibit no systematic effects on evolutionary potential, highlighting the robustness of digital evolution. Combined, these observations enhance our understanding of how instruction architecture impacts evolutionary potential, enabling the creation of architectures that support more rapid evolution of complex solutions to a broad range of challenges.

Dataset Information

An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells.

Publications

An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells.

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