Software Defined Radio Implementation of a Digital Self-interference Cancellation Method for Inband Full-Duplex Radio Using Mobile Processors
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New means to improve spectral efficiency and flexibility in radio spectrum use are in high demand due to congestion of the available spectral resources. Systems deploying inband full-duplex transmission aim at providing higher spectral efficiency by concurrent transmission and reception at the same frequency. Potentially doubling system throughput, full-duplex communications is considered as an enabler technology for the upcoming 5G networks. However, system performance is degraded due to the strong self-interference (SI) caused by overlapping of high power transmit signal with the received signal of interest. Furthermore, due to commonly existing radio frequency imperfections, advanced techniques capable of mitigating nonlinear SI are required. This article presents a real-time software-defined implementation of a digital SI canceller for full-duplex transceivers, potentially applicable even in mobile-scale devices. Recently, software-defined radio has gained a lot of interest due to its higher flexibility, scalability, and shorter time-to-market cycles compared to traditional fixed-function hardware designs. Moreover, as the performance enhancements achieved by increasing the clock frequency is reaching its limits, the current trend is towards multi-core processors. Since contemporary mobile phones already contain powerful massively parallel GPUs and CPUs, feasibility of a real-time implementation on mobile processors is studied. The reported results show that by adopting the presented solution, it is possible to achieve sufficient SI cancellation under time varying coupling channel conditions. Additionally, the possibility of carrying out such advanced processing in a real-time fashion on the selected platforms is investigated, and the implementation is evaluated in terms of execution time, power, and energy consumption.
Keywords5G Full-duplex Self-interference cancellation GPU OpenCL
This work was supported by Tampere University of Technology graduate school, and the Academy of Finland via projects “In-Band Full-Duplex Radio Technology: Realizing Next Generation Wireless Transmission” (304147) and “Making Programmable Logic Feasible in the Cloud.” (297548).
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