Cyclostationary Analysis of Analog Least Mean Square Loop for Self-Interference Cancellation in In-Band Full-Duplex Systems

Le, Anh Tuyen; Tran, Le Chung; Huang, Xiaojing

doi:10.1109/lcomm.2017.2755024

Cited by 17 publications

(9 citation statements)

References 8 publications

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“…Considering the degradation factor given in [24], the practical results agree with the theoretical ones. The level of cancellation is also measured with different roll-off factors of the pulse shaping filter to confirm the analyses shown in [22]. Finally, the prototype is evaluated with a 20 MHz-bandwidth orthogonal frequency-division multiplexing (OFDM) signal to confirm that the ALMS loop works well in both single carrier and multicarrier signaling systems as mentioned in [21,22,24].…”

mentioning

confidence: 73%

“…Denoting u l (t) = h l − w l (t)e j2π f c lT d as the difference between the channel coefficient and the weighting coefficient in the l-th tap of the ALMS loop, we can see that the power of u l (t) indicates the performance of the ALMS loop. By evaluating u l (t) in different scenarios, the behaviors of the ALMS loop have been investigated and published in [12,19,[21][22][23][24].…”

Section: Alms Loop Architecturementioning

confidence: 99%

“…The promising application of the ALMS loop in IBFD MIMO systems has been presented in [20] with a beam-based analog SI cancellation structure. The behaviors of this adaptive filter are comprehensively investigated in [12,19,[21][22][23][24]. It has been shown that the performance of the ALMS loop depends on the transmitted signal properties and the structure of the loop.…”

Section: Introductionmentioning

confidence: 99%

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Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective

Tran

Huang

et al. 2020

Sensors

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Self-interference (SI) is the key issue that prevents in-band full-duplex (IBFD) communications from being practical. Analog multi-tap adaptive filter is an efficient structure to cancel SI since it can capture the nonlinear components and noise in the transmitted signal. Analog least mean square (ALMS) loop is a simple adaptive filter that can be implemented by purely analog means to sufficiently mitigate SI. Comprehensive analyses on the behaviors of the ALMS loop have been published in the literature. This paper proposes a practical structure and presents an implementation of the ALMS loop. By employing off-the-shelf components, a prototype of the ALMS loop including two taps is implemented for an IBFD system operating at the carrier frequency of 2.4 GHz. The prototype is firstly evaluated in a single carrier signaling IBFD system with 20 MHz and 50 MHz bandwidths, respectively. Measured results show that the ALMS loop can provide 39 dB and 33 dB of SI cancellation in the radio frequency domain for the two bandwidths, respectively. Furthermore, the impact of the roll-off factor of the pulse shaping filter on the SI cancellation level provided by the prototype is presented. Finally, the experiment with multicarrier signaling shows that the performance of the ALMS loop is the same as that in the single carrier system. These experimental results validate the theoretical analyses presented in our previous publications on the ALMS loop behaviors.

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confidence: 73%

Section: Alms Loop Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective

Tran

Huang

et al. 2020

Sensors

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“…The simulation results show that the 1AOA/nRSSI LS estimator is suitable when more accurate RSSI measurements are available, while the subspace method is more suitable in a shadowing environment with an acceptable increase in the number of anchors and computational complexity. Possible future directions include improving the precision of 1AOA/nRSSI localization algorithms using different approaches listed in Section 4 and adopting an analog least mean square loop presented in [29][30][31][32] for a more accurate ranging estimation. Additionally, we might consider orthogonal frequency division multiplexing (OFDM) for correlated multipath fading channels [33] in the localization and positioning contexts.…”

Section: Discussionmentioning

confidence: 99%

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Tran

Huang

et al. 2020

Sensors

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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

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“…Since stationary noise does not have cyclostationarity in the case where cyclic frequency is not equal to zero, and the theoretical spectrum correlation function is zero [28]. The use of cyclic spectrum correlation in signal analysis and processing can eliminate some random factors coming from the signal itself, such as additive white Gaussian noise.…”

Section: Parameter Estimation Based On Cyclic Spectrummentioning

confidence: 99%

Carrier frequency and symbol rate estimation based on cyclic spectrum

Cao¹,

Zhang²

2020

JSEE

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Carrier frequency and symbol rate estimation are the main contents of parameter estimation, which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication. With the development of wireless communication, the signal transmission environment has become increasingly bad, causing more difficulties in parameter estimation. It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related. In practice, it is impossible to calculate the cyclic spectrum of infinite length data signals. When using finite length data to obtain a cycle spectrum, the truncation noise is induced, resulting in interference. It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines. An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.

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Cyclostationary Analysis of Analog Least Mean Square Loop for Self-Interference Cancellation in In-Band Full-Duplex Systems

Cited by 17 publications

References 8 publications

Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective

Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective

Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks

Carrier frequency and symbol rate estimation based on cyclic spectrum

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