No abstract

Spectrum pooling is a resource sharing strategy, which allows a license owner to share a sporadically used part of his licensed spectrum with a renter system, until he needs it himself. For a frictionless operation of a spectrum pooling system, the license owner has to have the absolute priority to access the shared spectrum. This means, the renter system has to monitor the channel and extract the channel allocation information (CAI), i.e. it has to detect, which parts of the shared spectrum the owner system accesses to, in order to immediately vacate the frequency bands being required by the license owner and to gain access to the frequency bands, which the license owner has stopped using. This paper proposes using cyclic feature detection for the extraction of the CAI in a specific spectrum pooling scenario, where the license owner is a GSM network and the spectrum renter is an OFDM based WLAN system.

This letter proposes two novel algorithms for the identification of quadrature amplitude modulation (QAM) signals. The cyclostationarity-based features used by these algorithms are robust with respect to timing, phase, and frequency offsets, and phase noise. Based on theoretical analysis and simulations, the identification performance of the proposed algorithms compares favorably with that of alternative approaches.

No abstract

Reconfigurable Software Radio equipment is seen as the key technology in the evolution of mobile communications. One of the most important properties of a Software Radio terminal is that it is capable of using a wide range of air interface standards, providing a seamless interoperability between different standards and an enhanced roaming capability. One of the key functions supporting this multimode operation is the air interface recognition. A Software Radio terminal has to be capable of detecting, recognizing and monitoring the air interfaces available in the frequency environment. In our paper, we propose exploiting the distinct cyclostationary properties of different air interface signals as features for air interface recognition.