An End-to-End Multi-Standard OFDM Transceiver Architecture Using FPGA Partial Reconfiguration

An End-to-End Multi-Standard OFDM Transceiver Architecture Using FPGA Partial Reconfiguration
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使用 FPGA 部分重配置的端到端多标准 OFDM 收发器架构

DOI:
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发表时间:
2017
期刊:
影响因子:
3.9
通讯作者:
I. Mcloughlin
I. Mcloughlin
中科院分区:
计算机科学3区
文献类型:
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作者:
T. Pham;Suhaib A. Fahmy;I. Mcloughlin

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随着对更高带宽和高效频谱使用需求的增加,能够根据环境条件和频谱要求跨多种标准运行的认知无线电变得越来越重要。传统的定制ASIC实现无法支持这种灵活性,因为标准的变化速度更快,而基带通信的软件实现无法达到性能和延迟要求。现场可编程门阵列(fpga)提供了一个结合灵活性、性能和效率的硬件平台,因此,它们已成为满足灵活的基于标准的认知无线电实现需求的关键。本文提出了一种动态可重构的端到端收发器基带,该基带可以在三种流行的OFDM标准(IEEE 802.11、IEEE 802.16和IEEE 802.22)之间切换,以非连续方式快速切换。我们表明,与传统方法相比,将FPGA部分重构与参数化模块相结合,可将重构时间减少71%,FIFO大小减少25%,并在重构期间提供缓冲数据的能力,以防止链路中断。基带公开了一个简单的接口,最大限度地提高了与不同认知引擎实现的兼容性。
Cognitive radios that are able to operate across multiple standards depending on environmental conditions and spectral requirements are becoming more important as the demand for higher bandwidth and efficient spectrum use increases. Traditional custom ASIC implementations cannot support such flexibility, with standards changing at a faster pace, while software implementations of baseband communication fail to achieve performance and latency requirements. Field programmable gate arrays (FPGAs) offer a hardware platform that combines flexibility, performance, and efficiency, and hence, they have become a key in meeting the requirements for flexible standards-based cognitive radio implementations. This paper proposes a dynamically reconfigurable end-to-end transceiver baseband that can switch between three popular OFDM standards, IEEE 802.11, IEEE 802.16, and IEEE 802.22, operating in non-contiguous fashion with rapid switching. We show that combining FPGA partial reconfiguration with parameterized modules offers a reduction in reconfiguration time of 71% and an FIFO size reduction of 25% compared with the conventional approaches and provides the ability to buffer data during reconfiguration to prevent link interruption. The baseband exposes a simple interface which maximizes compatibility with different cognitive engine implementations.