Realtime Software Defined Self-Interference Cancellation Based on Machine Learning for In-Band Full Duplex Wireless Communications

Guo, Hanqing; Xu, Jingshi; Zhu, Shangyue; Wu, Shaoen

doi:10.1109/iccnc.2018.8390351

Cited by 22 publications

(18 citation statements)

References 7 publications

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“…Machine learning (ML) and deep learning are becoming a ubiquitous solution in wireless communications, e.g., in blind detection of modulation orders [78], modulation classification [79], [80], real-time interference cancellation [81], precoding design in MIMO networks [82], and so on. ML could be used in real-time SDR-based experiments [81] or for offline processing. Real-time application in the context of NOMA could be SIC and synchronization.…”

Section: Signal Processing Using Machine Learningmentioning

confidence: 99%

Over-the-Air Implementation of NOMA: New Experiments and Future Directions

Zhang

Vaezi

2021

IEEE Access

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Non-orthogonal multiple access (NOMA) is widely recognized to increase the number of users and enhance the spectral efficiency in fifth-generation (5G) wireless networks and beyond. NOMA is still in the theoretical analysis and simulation phases and fewer experimental works are reported to date. In this paper, we design and implement NOMA in software-defined radio, and evaluate its performance. This includes the real-time realization of the key components of NOMA, i.e., superposition and successive interference cancellation. The main novelty of this paper is to introduce constructing superimposed signals with varying symbol rates to enlarge the achievable rate region of the experimented NOMA. By applying varying symbol rates, the set of possible transmission rate pairs enlarges and we can reach higher data rates compared to existing modulation and coding schemes (MCS). We also propose an algorithm to efficiently find the rate pairs. Simulations and experiments demonstrate that NOMA with a varying symbol rate not only can reach higher data rates than orthogonal schemes such as time division multiple access, but it can also outperform existing MCS-based methods which have a fixed symbol rate. The experiments also show that there is a noticeable gap between NOMA in theory and practice. In addition to the new NOMA experiments, we review the state-of-the-art in experimental NOMA. We also discuss several directions for future experiments that can help bridge the gap between theory and practice and bring NOMA to practical communication systems.

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Section: Signal Processing Using Machine Learningmentioning

confidence: 99%

Over-the-Air Implementation of NOMA: New Experiments and Future Directions

Zhang

Vaezi

2021

IEEE Access

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“…Currently, various approaches have been proposed in the existing works to suppress SI and enhance SE, such as antenna, analog, and digital cancellation [4][5][6][7][8][9][10][11]. Generally, antenna cancellation is aimed at increasing the isolation between transmission and reception [5].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, antenna cancellation is aimed at increasing the isolation between transmission and reception [5]. Analog cancellation is used to suppress the SI power by combining a reference SI signal in which the phase and amplitude are adjusted [6,7,11]. However, because of several physical constraints upon antenna design and inaccuracies in obtaining SI signals in analog circuits, residual SI remains powerful in the desired signal [12].…”

Section: Introductionmentioning

confidence: 99%

A Lazy Learning-Based Self-Interference Cancellation Approach for In-Band Full-Duplex Wireless Communication Systems

Zhao

Liao

et al. 2022

Wireless Communications and Mobile Computing

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We propose a new lazy learning-based cancellation approach to improve spectral efficiency for current wireless communication systems, suppress self-interference (SI) sent from base stations, and enable in-band full-duplex (IBFD) transmissions in cellular networks. Our proposed approach consists of two phases based on traditional IBFD systems: an offline phase for database generation and an online phase for data transmission. In the offline phase, the output before a 0/1 decision is premeasured without the desired signal input and recorded in a database with self-defined feature vectors (FVs). In the online phase, a suitable result is sought from the generated database with the help of a learning method and FV for the same system architecture with the desired signal input. The result is then assigned an SI cancellation value. Regular and eager learning-based cancellation approaches are employed to evaluate the proposed method and simulate the transmission output. Computer simulation results indicated that the proposed cancellation methods could achieve about 134 dB SI suppression and achieve nearly the same transmission levels as methods with no SI effect, enabling the IBFD operations in wireless communication systems better than the regular and eager learning-based techniques.

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“…Multipath-rich channels have been modeled for the purposes of band-allocation prediction in cognitive radio deployments [22], and much focus has been placed on neural networks reducing computational complexity in nonlinear digital cancellation schemes [23], [24]. The latter has also been extended to a software-defined radio (SDR) platform [25] that has expanded to the use of deep neural networks [26]. Furthermore, studies have been conducted on the effects of real and complex data samples for digital cancellation [27] as well as the hardware resources required for these approaches in comparison to traditional polynomial-based nonlinear cancellation [28].…”

Section: Introductionmentioning

confidence: 99%

RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems

Kolodziej¹,

Cookson²,

Perry³

2021

IEEE Open J. Commun. Soc.

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The fifth-generation wireless system framework provides the option to evaluate the performance of in-band full-duplex (IBFD) operation through flexible duplexing. The resulting self-interference, however, must be mitigated within a fraction of a symbol duration for successful communication. This paper introduces the use of neural network machine learning to accelerate the tuning of multi-tap adaptive RF cancellers. Additionally, the optimal network configurations, input data structures and training dataset densities that optimize the performance of this technique are presented. The tuning results of a prototype system using a two-tap canceller were measured over 20 and 100 MHz bandwidths centered at 2.5 GHz, and demonstrated averages of 40 dB cancellation and 6 tuning iterations. These results are compared to a survey of previously-reported adaptive cancellers, and illustrate that this novel application of machine learning to RF canceller tuning provides the fastest convergence speed to date, which can enable IBFD operation in dynamic interference environments.

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Realtime Software Defined Self-Interference Cancellation Based on Machine Learning for In-Band Full Duplex Wireless Communications

Cited by 22 publications

References 7 publications

Over-the-Air Implementation of NOMA: New Experiments and Future Directions

Over-the-Air Implementation of NOMA: New Experiments and Future Directions

A Lazy Learning-Based Self-Interference Cancellation Approach for In-Band Full-Duplex Wireless Communication Systems

RF Canceller Tuning Acceleration Using Neural Network Machine Learning for In-Band Full-Duplex Systems

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