Spatial DCT-Based Channel Estimation in Multi-Antenna Multi-Cell Interference Channels

This work addresses channel estimation in multiple antenna multicell interference-limited networks. Channel state information (CSI) acquisition is vital for interference mitigation. Wireless networks often suffer from multicell interference, which can be mitigated by deploying beamforming to spatially direct the transmissions. The accuracy of the estimated CSI plays an important role in designing accurate beamformers that can control the amount of interference created from simultaneous spatial transmissions to mobile users. Therefore, a new technique based on the structure of the spatial covariance matrix and the discrete cosine transform (DCT) is proposed to enhance channel estimation in the presence of interference. Bayesian estimation and Least Squares estimation frameworks are introduced by utilizing the DCT to separate the overlapping spatial paths that create the interference. The spatial domain is thus exploited to mitigate the contamination which is able to discriminate across interfering users. Gains over conventional channel estimation techniques are presented in our simulations which are also valid for a small number of antennas.Comment: Submitted for possible publication. arXiv admin note: text overlap with arXiv:1203.5924 by other author

Joint Channel Estimation and Pilot Allocation in Underlay Cognitive MISO Networks

Cognitive radios have been proposed as agile technologies to boost the spectrum utilization. This paper tackles the problem of channel estimation and its impact on downlink transmissions in an underlay cognitive radio scenario. We consider primary and cognitive base stations, each equipped with multiple antennas and serving multiple users. Primary networks often suffer from the cognitive interference, which can be mitigated by deploying beamforming at the cognitive systems to spatially direct the transmissions away from the primary receivers. The accuracy of the estimated channel state information (CSI) plays an important role in designing accurate beamformers that can regulate the amount of interference. However, channel estimate is affected by interference. Therefore, we propose different channel estimation and pilot allocation techniques to deal with the channel estimation at the cognitive systems, and to reduce the impact of contamination at the primary and cognitive systems. In an effort to tackle the contamination problem in primary and cognitive systems, we exploit the information embedded in the covariance matrices to successfully separate the channel estimate from other users' channels in correlated cognitive single input multiple input (SIMO) channels. A minimum mean square error (MMSE) framework is proposed by utilizing the second order statistics to separate the overlapping spatial paths that create the interference. We validate our algorithms by simulation and compare them to the state of the art techniques.Comment: 6 pages, 2 figures, invited paper to IWCMC 201