Abstract
In recent times, the evolution of cellular networks has an exponential growth. Millimeter-wave provides higher spectral efficiency and wider bandwidth; it solves the problem of adopting a greater number of users to the mobile network in the future. The beamforming in the mm-wave system is introduced to narrow down the beam which is highly directional and reduces the path loss in the systems. To deploy the beamforming in the existing systems, it requires more cost and performance also gets affected. To reduce the hardware cost, lens antenna array is used in the existing systems in order to reduce the RF chains and improve the performance. While deploying the large lens antenna with a smaller number of RF chains, channel estimation becomes more difficult and crucial. Recently, several deep learning-based channel estimation schemes for mm-Wave are proposed to improve the efficiency of the system. In order to improve the performance of the channel, sparsity of the beamspace channel is exploded. This results in considering the beamspace channel estimation as the sparse signal recovery problem. The sparse signal recovery problem can be solved by AMP which is the classic iterative-based algorithm. However, these systems do not satisfy the performance and accuracy. The modified GM-based LAMP system uses the prior information for the channel estimation to improve the performance.
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Baranidharan, V. et al. (2022). Modified Gaussian Mixture Distribution-Based Deep Learning Technique for Beamspace Channel Estimation in mmWave Massive MIMO Systems. In: Satyanarayana, C., Samanta, D., Gao, XZ., Kapoor, R.K. (eds) High Performance Computing and Networking. Lecture Notes in Electrical Engineering, vol 853. Springer, Singapore. https://doi.org/10.1007/978-981-16-9885-9_32
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DOI: https://doi.org/10.1007/978-981-16-9885-9_32
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