Hardware Implementation of Neural Self-Interference Cancellation

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1\times$ and $2\times$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.Comment: Accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and System

On the Implementation Complexity of Digital Full-Duplex Self-Interference Cancellation

In-band full-duplex systems promise to further increase the throughput of wireless systems, by simultaneously transmitting and receiving on the same frequency band. However, concurrent transmission generates a strong self-interference signal at the receiver, which requires the use of cancellation techniques. A wide range of techniques for analog and digital self-interference cancellation have already been presented in the literature. However, their evaluation focuses on cases where the underlying physical parameters of the full-duplex system do not vary significantly. In this paper, we focus on adaptive digital cancellation, motivated by the fact that physical systems change over time. We examine some of the different cancellation methods in terms of their performance and implementation complexity, considering the cost of both cancellation and training. We then present a comparative analysis of all these methods to determine which perform better under different system performance requirements. We demonstrate that with a neural network approach, the reduction in arithmetic complexity for the same cancellation performance relative to a state-of-the-art polynomial model is several orders of magnitude.Comment: Presented at the 2020 Asilomar Conference for Signals, Systems, and Computer

Augmentation of Self-Interference Cancellation for Full-Duplex using NARX Neural Networks

A self-interference cancellation augmentation technique based on a NARX (Nonlinear Autoregressive Exogenous) network model is implemented and evaluated on an OFDM-based full-duplex system testbed operating at 2.4 GHz. In a comparison with the state-of-the-art polynomial models, our experimental results demonstrate the significant computational efficiency of the proposed NARX model. Specifically, the NARX model with one hidden layer reduces computations by 83.3% while achieving the same cancellation level within a bandwidth of 2 MHz

Integrated approach for efficient power consumption and resource allocation in MIMO-OFDMA

The growing interest towards wireless communication advancement with smart devices has provided the desired throughput of wireless communication mechanisms. But, attaining high-speed data packets amenities is the biggest issue in different multimedia applications. Recently, OFDM has come up with the useful features for wireless communication however it faces interference issues at carrier level (intercarrier interferences). To resolve these interference issues in OFDM, various existing mechanisms were utilized cyclic prefix, but it leads to redundancy in transmitted data. Also, the transmission of this redundant data can take some more power and bandwidth. All these limitations factors can be removed from a parallel cancellation mechanism. The integration of parallel cancellation and Convolution Viterbi encoding and decoding in MIMO-OFDMA will be an effective solution to have high data rate which also associations with the benefits of both the architectures of MIMO and OFDMA modulation approaches. This paper deals with this integrated mechanism for efficient resource allocation and power consumption. For performance analysis, MIMO-OFDMA system is analyzed with three different approaches likeMIMO-OFDM system without parallel cancellation (MIMO-OFDMA-WPC), MIMO-OFDMA System with parallel cancellation (MIMO-OFDMA-PC) and proposed IMO-OFDMA system with parallel cancellation and Convolution Viterbi encoding/decoding (pMIMO-OFDMA-PC &CVed) for 4x4 transmitter and receiver. Through performance analysis, it is found that the proposed system achieved better resource allocation (bandwidth) with high data rate by minimized BER rate and achieved least power consumption with least BER