Effects of coupling and overspeeding on performance of predictor antenna systems in wireless moving relays

Abstract

Using predictor antenna systems for modern wireless moving relays and base stations on top of vehicles such as buses, trains etc. proves to be a reliable approach for collecting channel state information to such fast moving nodes. Recently, it has been shown that coupling between different ports of a multiport antenna system used as a part of a predictor system can reduce the prediction performance. In this study, by integrating position and velocity vectors in the channel covariance matrix as seen at the antenna ports in a rich multipath environment, the authors quantify the impact of antenna coupling on prediction performance. Moreover, practically these predictor systems are designed for a certain target velocity. They further quantify the adverse effect of velocities, different from the target velocity, on prediction performance. In case open-circuit decoupling is necessary, the sensitivity of the predictor antenna system performance with respect to the accuracy of the input network parameters is disclosed.

A Semi-Linear Approximation of the First-Order Marcum Q-Function With Application to Predictor Antenna Systems

“…6, 7). Thus, the throughput-optimized rate allocation can be well approximated by (50), and the semi-linear approximation of Theorem 1/Corollaries 1-2 is a good approach to study the considered optimization problem. • The deployment of the PA increases the throughput, compared to the open-loop setup, especially in moderate/high SNRs (Fig.…”

“…Our paper is different from the state-of-the-art literature because the proposed semi-linear approximation of the firstorder Marcum Q-function and the derived closed-form expressions for the considered integrals, i.e., Theorems 1-4 and Corollaries 1-2, have not been presented by, e.g., [1], [3]- [7], [9]- [51]. Also, as opposed to [43]- [50], we perform analytical performance evaluation of PA systems with CSIT (T: at the transmitter)-based rate optimization to mitigate the effect of the spatial mismatch. Moreover, compared to our preliminary results in [51], this paper develops the semilinear approximation method for the Marcum Q-function, and uses our proposed approximation method to analyze the performance of the PA system.…”

“…Moreover, the European project deliverables, e.g., [57,Chapter 2], [58, P. 107] and [59,Chapter 3], have well addressed the feasibility of the PA concept in network-level design. Finally, different works have analyzed the PA system in both frequency division duplex (FDD) [45], [46], [48] and time division duplex (TDD) [44], [49] systems, with some developments on addressing the system challenges such as antenna coupling [47], [50], spatial mismatch [50], [51], and spectrum underutilization [52], [53].…”

“…6-7 for more discussions). Finally, note that, one can involve additional independent Gaussian variables to model other error sources, e.g., imperfect antenna coupling [50], and follow the same procedure as in (58) to evaluate the performance.…”

Transmission of wireless backhaul signal in a cellular system with small moving cells

“…The M-BS can predict the ST-CSI of backhaul antennas by exploiting the ST-CSI of predictor antennas [16]. However, the correlation between the channels of the predictor antennas and the backhaul antennas may noticeably vary in the presence of mobility [19]. When the correlation is not large, the channel prediction may not be effective for the estimation of ST-CSI of the backhaul channel.…”