2015/05/08 by Maha Alodeh, Symeon Chatzinotas, Alodeh, Maha +3
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cognitive Radio Networks and Spectrum Sensing #FOS: Computer and information sciences #Information Theory (cs.IT) #Wireless Communication Networks Research
paper · pdf · doi:10.48550/arxiv.1505.02027
openalex publication_date 2015/05/08 · openalex created_date 2022/09/26 · openalex updated_date 2026/07/28
Cognitive radios have been proposed as agile technologies to boost the\nspectrum utilization. This paper tackles the problem of channel estimation and\nits impact on downlink transmissions in an underlay cognitive radio scenario.\nWe consider primary and cognitive base stations, each equipped with multiple\nantennas and serving multiple users. Primary networks often suffer from the\ncognitive interference, which can be mitigated by deploying beamforming at the\ncognitive systems to spatially direct the transmissions away from the primary\nreceivers. The accuracy of the estimated channel state information (CSI) plays\nan important role in designing accurate beamformers that can regulate the\namount of interference. However, channel estimate is affected by interference.\nTherefore, we propose different channel estimation and pilot allocation\ntechniques to deal with the channel estimation at the cognitive systems, and to\nreduce the impact of contamination at the primary and cognitive systems. In an\neffort to tackle the contamination problem in primary and cognitive systems, we\nexploit the information embedded in the covariance matrices to successfully\nseparate the channel estimate from other users' channels in correlated\ncognitive single input multiple input (SIMO) channels. A minimum mean square\nerror (MMSE) framework is proposed by utilizing the second order statistics to\nseparate the overlapping spatial paths that create the interference. We\nvalidate our algorithms by simulation and compare them to the state of the art\ntechniques.\n