Location-aware spectrum sensing for cognitive visible light communications over multipath channels

Abstract

Accurate spectrum sensing is crucial for cognitive visible light communication (CVLC). However, due to multiple light emitting diodes (LEDs) and indoor reflections, a CVLC channel often shows a multipath characteristic which is difficult for a secondary user (SU) to estimate before spectrum sensing, thereby leading to a degraded sensing accuracy. To tackle this issue, we propose a location-aware spectrum sensing scheme for the CVLC system, where its multipath channel can be effectively estimated based on the location information of an SU. By considering different a priori information, we propose three primary user (PU) detection algorithms. Specifically, we first propose a location-aware likelihood-ratio-test detection (LLD) algorithm to achieve an optimal sensing performance by using a priori knowledge of signal and noise variances. To reduce the computational complexity of LLD, we propose a more efficient location-aware semi-blind detection (LSD) algorithm, which requires no a priori knowledge of signal variance. Further, to tackle noise uncertainty, we propose a location-aware near-blind detection (LND) algorithm, which does not require either signal or noise variance. To evaluate the performance of the proposed algorithms, we also develop their respective analytical models based on the multipath channel. It is found that the analytical models can accurately match the results obtained by simulations, and LLD and LSD can improve detection probability by 10% compared with the conventional energy detection (ED) scheme. Moreover, by incorporating LSD with cyclic-prefix-based detection (CD), the detection probability can be further improved by ∼20% and 8% compared with ED and CD, respectively. Finally, LND outperforms all the other algorithms when the noise uncertainty is over 0.5 dB, and it is found that both LSD and LND are robust to receiver tilt and LSD is robust to location error.