Experiment-Driven Characterization of Full-Duplex Wireless Systems

Abstract

We present an experiment-based characterization of passive suppression and active self-interference cancellation mechanisms in full-duplex wireless communication systems. In particular, we consider passive suppression due to antenna separation at the same node, and active cancellation in analog and/or digital domain. First, we show that the average amount of cancellation increases for active cancellation techniques as the received self-interference power increases. Our characterization of the average cancellation as a function of the self-interference power allows us to show that for a constant signal-to-interference ratio at the receiver antenna (before any active cancellation is applied), the rate of a full-duplex link increases as the self-interference power increases. Second, we show that applying digital cancellation after analog cancellation can sometimes increase the self-interference, and thus digital cancellation is more effective when applied selectively based on measured suppression values. Third, we complete our study of the impact of self-interference cancellation mechanisms by characterizing the probability distribution of the self-interference channel before and after cancellation. I. INTRODUCTIONCurrent deployed wireless communication systems employ either a time-division or frequencydivision approach to bidirectional communication. This requires dividing the temporal and/or spectral resources into orthogonal resources and results in half-duplex wireless communication systems. The key deterrent in implementing a full-duplex wireless communication system, which

Superimposed signaling inspired channel estimation in full-duplex systems

“…For the SI channel, experimental results have shown that before passive and active cancellation, the SI channel has a strong LOS component and can be modeled as a Rician distribution with a large K factor (approximately 20-25 dB). After passive suppression and analog cancellation, the strong LOS component is significantly reduced but still present and can be modeled as a Ricean distribution with K = 0 dB [5]. Hence, we generate the SI channel as (17) where ζ is uniformly distributed angle of arrival of the LOS component of the SI channel [39].…”

“…Hence, we generate the SI channel as (17) where ζ is uniformly distributed angle of arrival of the LOS component of the SI channel [39]. For the simulations, the signal-to-interference-noise ratio (SINR) is given by [5] …”

“…As discussed in Section 1, even with state-of-the-art passive suppression and analog cancellation, the SIR can still be around − 5 dB [5,40]. Hence, we adopt this value of the SIR in the simulations while assuming that the communication channel has average energy of unity, i.e., E |h ba | 2 = σ 2 h ba = 1.…”

“…This intuition is confirmed from (16), which indicates that higher values of β are needed at low E b /N 0 to reach a given estimation error. Furthermore, since the lower bound on the estimation error also decreases with N, the minimum value of β found for smaller N can also serve for larger N. Since the experimental results of [5,41] show that the FD communication channel is normally constant for more than N > 128 symbols, we propose to find the minimum β at N = 128 and E b /N 0 = 0 dB. Figure 3 shows the MSE performance of the proposed technique versus β for E b /N 0 = 0 dB, N = 128, and SIR = − 50 dB.…”

“…In formulating the problem, we make the following assumptions: (i) the transmitter is aware of its own signal, i.e., x a is known at node a, which is a commonly adopted assumption in the literature [3,5], (ii) the interference channel h aa and the communication channel h ba are unknown deterministic parameters, (iii) the transmit symbol from node b is modeled using a discrete random distribution, and (iv) we observe N independent received symbols. The blind channel estimation problem requires the knowledge of the joint probability density function (PDF) of all observations, which is derived from the conditional PDF of a single observation.…”