DSIC: Deep Learning Based Self-Interference Cancellation for In-Band Full Duplex Wireless
DSIC: Deep Learning Based Self-Interference Cancellation for In-Band Full Duplex Wireless
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DOI：
10.1109/globecom38437.2019.9013521
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发表时间：
2018-11
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影响因子：
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通讯作者：
Hanqing Guo;N. Zhang;Saeed AlQarni;Shaoen Wu
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文献类型：
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作者：
Hanqing Guo;N. Zhang;Saeed AlQarni;Shaoen Wu
带内全双工（IBFD）无线技术由于其频谱利用率的巨大潜力，在未来的无线通信和网络中受到极大的关注。然而，IBFD无线技术的关键挑战是自干扰。因此，有效的自干扰消除是实现IBFD无线的关键.本文提出了一种实时非线性自干扰消除解决方案：基于深度学习的自干扰消除（DSIC），以实现IBFD无线。在该解决方案中，自干扰信道由深度神经网络（DNN）建模。首先收集同步的自干扰信道数据以训练自干扰信道的DNN。之后，训练的DNN用于消除无线节点处的自干扰。该解决方案已在USRP SDR测试平台上实现，并在真实的世界中进行了评估，在传输包括数字、文本以及图像在内的信息时具有各种调制。这导致在数字消除中的17dB的性能，这非常接近于自干扰功率并且几乎消除了测试床中的SDR节点处的自干扰。该解决方案在许多情况下和不同的调制方案下产生平均8.5%的误码率（BER）。
In-band full duplex (IBFD) wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, how- ever, is throttled by its key challenge, namely self-interference. Therefore, effective self- interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non- linear self-interference cancellation solution: Deep learning based Self-Interference Cancellation (DSIC) to enable IBFD wireless. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self- interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self- interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.