Low-latency and high-throughput software turbo decoders on multi-core architectures

In the last few years, with the advent of a software-defined radio (SDR), the processor cores were stated to be an efficient solution to execute the physical layer components. Indeed, multi-core architectures provide both high-processing performance and flexibility, such that they are used in current base station systems instead of dedicated FPGA or ASIC devices. Currently, an extension of the SDR concept is running. Indeed, cloud platforms become attractive for the virtualization of radio access network functions. Actually, they improve the efficiency of the computational resource usage, and thus the global power efficiency. However, the implementation of a physical layer on a Cloud-RAN platform as discussed by Wubben and Paul (2016); Checko et al. (JAMA 17(1):405–426, 2015); Inc (2015); and Wubben et al. (JAMA 31(6):35–44, 2014) or FlexRAN platform as discussed by Wilson (2018); Foukas et al. (2017); Corp. (2017); Foukas et al. (2016) is a challenging task according to the drastic latency and throughput constraints as discussed by Yu et al. (2017) and Parvez (2018). Processing latencies from 10 μ s up to hundred of μ s are required for future digital communication systems. In this context, most of works about software implementations of ECC applications is based on massive frame parallelism to reach high throughput. Nonetheless, they produce unacceptable decoding latencies. In this paper, a new turbo decoder parallelization approach is proposed for x86 multi-core processors. It provides both: high-throughput and low-latency performances. In comparison with all CPU- and GPU-related works, the following results are observed: shorter processing latency, higher throughput, and lower energy consumption. Regarding to the best state-of-the-art x86 software implementations, 1.5 × to 2 × throughput improvements are reached, whereas a latency reduction of 50 × and an energy reduction of 2 × are observed.