Abstract
This article investigates channel estimation problem in massive MIMO partially centralized cloud-RAN (MPC-RAN). The channel estimation was realized through compressed data method to minimize the huge pilot overhead, then combined with parallel Givens data projection method (PGDPM) to form a semi-blind estimator. Comparison and analysis of improved minimum mean square error (MMSE), fast data projection method (FDPM), compressed data, and PGDPM techniques was evaluated for achievable normalized mean square error (NMSE) in MPC-RAN. The PGDPM-based estimator had the lowest normalized mean square error. The FDPM and PGDPM based methods are comparable in performance with PGDPM based estimator having a slight edge over FDPM-based estimator. This vindicates PGDPM-based estimator as a method to be utilized in channel estimation since it compresses the massive MIMO channel information, hence mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal BBU resource utilization.
Highlights
For a couple of decades, optimal use of the restricted amount of accessible spectrum to consider the exponentially increasing interest in throughput has been the focal point of communication systems and signal processing
This vindicates parallel Givens data projection method (PGDPM)-based estimator as a method to be utilized in channel estimation since it compresses the massive Multiple-Input Multiple-Output (MIMO) channel information, mitigating the fronthaul finite capacity problem, and at the same time, it is geared towards efficient parallelization for optimal baseband unit (BBU) resource utilization
We look at the performance indicators normalized mean square error (NMSE), SNR, reuse fact f and M for all the channel estimation techniques viz improved minimum mean square error (MMSE), compressed data and PGDPM
Summary
For a couple of decades, optimal use of the restricted amount of accessible spectrum to consider the exponentially increasing interest in throughput has been the focal point of communication systems and signal processing. One of the anticipated fronthaul finite capacity solutions is to break functions such that some are performed at the RRH and others at the baseband unit (BBU) Taking this suggested architecture into account, the RRH is tasked with performing basic functions such as beamforming and the BBU is left to perform digital functions like channel estimation. This makes fronthaul traffic largely dependent on user terminals (UT) data rates and not on antenna numbers Park, Kim, Carvalho, & Manch, 2017) This results in massive MIMO partially centralized cloud-radio access (MPC-RAN) network H. Park, Simeone, Sahin, & Shamai, 2014)
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