Blind Null-Space Learning for MIMO Underlay Cognitive Radio with Primary User Interference Adaptation

Abstract

This paper proposes a blind technique that enables a MIMO cognitive radio Secondary User (SU) to transmit in the same band simultaneously with a Primary User (PU) by utilizing separate spatial dimensions than the PU. Specifically, the SU transmits in the null space of the interference channel to the PU. The SU learns this null space without burdening the PU with any knowledge or explicit cooperation with the SU. The only condition required is that during the learning period, the SU is allowed to inflict "non-harmful" interference to the PU. The SU measures a monotonic function of this interference in order to learn the null space. Specifically, during the learning interval, the SU learns the null space by iteratively modifying the spatial orientation of its transmitted signal and measures the effect of this modification on the monotonic function that it observes. We provide simulation results demonstrating that the algorithm converges rapidly and is robust to quantization noise and other sources of interference.Index Terms-Underlay cognitive radio, cognitive radio (CR), MIMO, null space, learning, blind, interference constraint, interference suppression, opportunistic spectrum sharing, spatial multiplexing.

Channel direction information probing for multi-antenna cognitive radio system

“…These imperfections have been carefully discussed in [4] for the probing of CGIsp, and the conclusions show that the probing performance is robust to small imperfections. In general, these imperfections are not unique to the problem studied here, but widely observed to exist in many other probing schemes for the CR system [3,8,9,12]. Therefore, to concentrate on the idea of the CDIsp probing itself, we leave these imperfections fuzzy in the design, and assume that the acquired hidden feedback information is perfect at the ST in Section 3 and 4.…”

“…To reduce the cooperation requirement between primary and secondary systems, several channel probing schemes [7][8][9][10] have been proposed for the ST to acquire the channel gain information from the ST to the PR (CGIsp). In [8], the ST is viewed as a relay and forwards the known useful signals to the PR in the probing.…”

“…For example, the channel gain associated with the CGIsp can be measured directly with the strength of link adaptation in the hidden feedback information [7], while the channel direction associated with the CDIsp can not. The work in [9,12] has proposed the algorithms to learn the null space information of the CDIsp from the hidden feedback information in primary system, which however, rely on the binary-search method. The binary-search method requires to adapt the probing signals with the received hidden feedback information until the acquired information is converged, which may cause too much overhead because of the iteration between the probing and acquisition.…”

Interference Alignment Based on Rank Constraint in MIMO Cognitive Radio Networks

“…So far, many works have studied applying IA to the CR networks [13][14][15][16][17][18][19][20][21][22][23]. In [13], the author characterized the achievable degree of freedom (DoF) for the secondary network.…”

“…In [18], a space-time opportunistic IA technique was proposed for the overlay CR network, where the PU used space-time water-filling algorithm to optimize its transmission and in the process, freed up unused eigenmodes that could be exploited by the SU. The authors of [20] proposed an algorithm that enables the secondary transmitter to learn the interference channel of the primary receiver by measuring a monotonic function of the interference to the primary receiver. The authors of [21] put forward a blind opportunistic IA method with which the SU is able to compute blindly the required channel state information (CSI) without interaction with the primary network.…”