Modem Design in the Era of 5G and Beyond: The Need for a Formal Approach

Wittig, Robert; Goens, Andrés; Menard, Christian; Matúš, Emil; Fettweis, Gerhard; Castrillón, Jerónimo

doi:10.1109/ict49546.2020.9239539

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Wittig et al [18] pointed out the new performance demands and increasing parameter space in new generations of mobile networks. They showed that the use of FPGA and partial reconfiguration in communication applications could significantly improve the system's energy efficiency and reduce the sub-frame drop rate, due to the workload's adaptivity.…”

Section: Related Workmentioning

confidence: 99%

A Partial-Reconfiguration-Enabled HW/SW Co-Design Benchmark for LTE Applications

Hosseinghorban

Kumar

2022

Electronics

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Rapid and continuous evolution in telecommunication standards and applications has increased the demand for a platform with high parallelization capability, high flexibility, and low power consumption. FPGAs are known platforms that can provide all these requirements. However, the evaluation of approaches, architectures, and scheduling policies in this era requires a suitable and open-source benchmark suite that runs on FPGA. This paper harnesses high-level synthesis tools to implement high-performance, resource-efficient, and easy-maintenance kernels for FPGAs. We provide various implementations of each kernel of PHY-Bench and WiBench, which are the most well-known benchmark suites for telecommunication applications on FPGAs. We analyze the execution time and power consumption of different kernels on ARM processors and FPGA. We have made all sources and documentation public for the benefit of the research community. The codes are flexible, and all kernels can easily be regenerated for different sizes. The results show that the FPGA can increase the speed by up to 19.4 times. Furthermore, we show that the power consumption of the FPGA can be reduced by up to 45% by partially reconfiguring a kernel that fits the size of the input data instead of using a large kernel that supports all inputs. We also show that partial reconfiguration can improve the execution time for processing a sub-frame in the uplink application by 33% compared to an FPGA-based approach without partial reconfiguration.

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Section: Related Workmentioning

confidence: 99%

A Partial-Reconfiguration-Enabled HW/SW Co-Design Benchmark for LTE Applications

Hosseinghorban

Kumar

2022

Electronics

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“…Modem baseband algorithms need to be carefully designed in 5G new radio (NR) to realize physical layer operations at a reduced complexity [1]. Channel estimation is an important operation, as the estimated channel is used for subsequent operations like equalization and interference whitening.…”

Section: Introductionmentioning

confidence: 99%

Learning-aided joint time-frequency channel estimation for 5G new radio

Myers

Kwon

Ding

et al. 2021

2021 IEEE Global Communications Conference (GLOBECOM)

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In this paper, we propose a learning-aided signal processing solution for channel estimation in 5G new radio (NR). Channel estimation is an important algorithm for baseband modem design. In 5G NR, estimating the channel is challenging due to two reasons. First, the pilot signals are transmitted over a small fraction of the available time-frequency resources. Second, the real time nature of physical layer processing introduces a strict limitation on the computational complexity of channel estimation. To this end, we propose a channel estimation technique that integrates a small one hidden layer neural network between two linear minimum mean squared error (LMMSE) interpolation blocks. While the neural network leverages the advantages of offline data-driven learning, the LMMSE blocks exploit the second order online channel statistics along time and frequency dimensions. The training procedure tunes the weights of the neural network by back-propagating through the time domain LMMSE interpolation block. We derive bounds on the training loss with the proposed method and show that our approach can improve the channel estimate.

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