This paper proposes a new algorithm for MIMO cognitive radio Secondary Users
(SU) to learn the null space of the interference channel to the Primary User
(PU) without burdening the PU with any knowledge or explicit cooperation with
the SU.
The knowledge of this null space enables the SU to transmit in the same band
simultaneously with the PU by utilizing separate spatial dimensions than the
PU. Specifically, the SU transmits in the null space of the interference
channel to the PU. We present a new algorithm, called the One-Bit Null Space
Learning Algorithm (OBNSLA), in which the SU learns the PU's null space by
observing a binary function that indicates whether the interference it inflicts
on the PU has increased or decreased in comparison to the SU's previous
transmitted signal. This function is obtained by listening to the PU
transmitted signal or control channel and extracting information from it about
whether the PU's Signal to Interference plus Noise power Ratio (SINR) has
increased or decreased.
In addition to introducing the OBNSLA, this paper provides a thorough
convergence analysis of this algorithm. The OBNSLA is shown to have a linear
convergence rate and an asymptotic quadratic convergence rate. Finally, we
derive bounds on the interference that the SU inflicts on the PU as a function
of a parameter determined by the SU. This lets the SU control the maximum level
of interference, which enables it to protect the PU completely blindly with
minimum complexity. The asymptotic analysis and the derived bounds also apply
to the recently proposed Blind Null Space Learning Algorithm.

本文提出了一种新的算法，称为一比特零空间学习算法（OBNSLA），用于 MIMO 认知无线电次用户（SU）学习主用户（PU）的干扰通道的零空间，无需给 PU 带来任何知识或显示与 SU 的合作。