A Maximum-Likelihood Channel Estimator for Self-Interference Cancelation in Full-Duplex Systems

Abstract

Operation of full-duplex systems requires efficient mitigation of the self-interference signal caused by the simultaneous transmission/reception. In this paper, we propose a maximum-likelihood (ML) approach to jointly estimate the selfinterference and intended channels by exploiting its own known transmitted symbols and both the known pilot and unknown data symbols from the other intended transceiver. The ML solution is obtained by maximizing the ML function under the assumption of Gaussian received symbols. A closed-form solution is first derived, and subsequently an iterative procedure is developed to further improve the estimation performance at moderate to high signal-to-noise ratio (SNR). We establish the initial condition to guarantee the convergence of the iterative algorithm to the ML solution. In the presence of considerable phase noise from the oscillators, a phase noise estimation method is proposed and combined with the ML channel estimator to mitigate the effects of the phase noise. Illustrative results show that the proposed methods offer good cancellation performance close to the Cramer-Rao bound (CRB) .Index Terms-Full duplex communication, channel estimation, maximum likelihood, iterative method, self-interference suppression, MIMO. 0018-9545 (c)

Superimposed signaling inspired channel estimation in full-duplex systems

“…We consider the received signal available at the output of the analog-to-digital converter (ADC). The received signal at node a is given by 1 [13,18]. In this paper, we focus on the estimation of linear parameters, while the estimation of non-linear parameters can be the topic of future works.…”

“…Thus, accurate digital SI cancellation is required to bring the SI as close to the noise floor as possible. The effectiveness of any digital interference cancellation technique depends strongly on the quality of the available channel estimates for both the SI and desired communication channels [12][13][14]. Typically, the baseband channels are estimated by using dataaided channel estimation techniques, where a portion of the data frame is allocated for known training sequences or pilot symbols [13,[15][16][17].…”

“…The effectiveness of any digital interference cancellation technique depends strongly on the quality of the available channel estimates for both the SI and desired communication channels [12][13][14]. Typically, the baseband channels are estimated by using dataaided channel estimation techniques, where a portion of the data frame is allocated for known training sequences or pilot symbols [13,[15][16][17]. In this regard, a maximumlikelihood (ML) approach was proposed in [13] to jointly estimate the residual SI and communication channels by exploiting the known transmitted symbols and both the known pilot and unknown data symbols from the other intended transceiver.…”

“…Typically, the baseband channels are estimated by using dataaided channel estimation techniques, where a portion of the data frame is allocated for known training sequences or pilot symbols [13,[15][16][17]. In this regard, a maximumlikelihood (ML) approach was proposed in [13] to jointly estimate the residual SI and communication channels by exploiting the known transmitted symbols and both the known pilot and unknown data symbols from the other intended transceiver. Another approach was proposed in [18] where a sub-space-based algorithm was developed to jointly estimate the residual SI and communication channels.…”

Outage probability of multi‐hop networks with amplify‐and‐forward full‐duplex relaying

“…Among these techniques, the selfinterference can be canceled by estimating the self-interference channels [41,42] or be suppressed with the null-space method in MIMO [43]. However, the estimation error and the trade-off between suppression and user rate still lead to RSI.…”