A Robust Channel Estimation Scheme for 5G Massive MIMO Systems

Abstract

Channel state information (CSI) feedback in massive MIMO systems is too large due to large pilot overhead. It is due to the large channel matrix dimension which depends on the number of base station (BS) antennas and consumes the majority of scarce radio resources. To solve this problem, we proposed a scheme for e cient CSI acquisition and reduced pilot overhead. It is based on the separation mechanism for the channel matrix. e spatial correlation among multiuser channel matrices in the virtual angular domain is utilized to split the channel matrix. en, the two parts of the matrix are estimated by deploying the compressed sensing (CS) techniques. is scheme is novel in the sense that the user equipment (UE) directly transmits the received symbols from the BS to the BS, so a joint CSI recovery is performed at the BS. Simulation results show that the proposed channel estimation scheme e ectively estimates the channel with reduced pilot overhead and improved performance as compared with the state-of-theart schemes.

Performance Analysis of Channel Estimation in 5G millimeter-Wave-MIMO Heterogeneous Systems

“…For blind and semi-blind CE techniques, the basic idea is to utilize certain inherent features in a modulated signal or exploit a decision feedback method rather than many pilots to acquire the CSI [18][19]. The accurate CSI feedback measurement from the User Equipment (UE) to BS was assumed, which is practically impossible [25]. Paulo R. B. Gomes et al [26] formulated two tensor-based semi-blind RXs-one iterative alternating Least Squares (LS) in nature and one closed form solution for joint DL (Downlink) and UL CE.…”

“…In the last, the conclusion is provided in section 5. (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) dB loss [39] and materials such as brick can weaken signals by as much as 40-80 dB [40]. The Doppler spread of over 3 KHz at 60 km/h at 60 GHz will change the channel in the order of hundreds of microseconds, which is much faster than today's cellular systems.…”

“…In paper[33], subcarrier groupingbased analysis cosparse recovery namely Reweighed Structured Analysis Cosparsity Update Subspace Pursuit (ReCoSUSP) algorithm was used for CE in wideband mm-Wave-MIMO output system. The paper[25] can be extended by incorporating mm-Wave with Ma-MIMO and performing the analysis of beamspace CE using the CS technique.…”

Unified Performance Analysis of Near and Far User in Downlink NOMA System over η - μ Fading Channel

“…The genie is assumed to provide every information about the system. Actually, such a genie is unrealistic in the practical sense, but genie-based systems are used to provide reference bounds for different performance metrics of communication systems [23], [24]. It is shown in [25] that side information provided by the genie helps in mitigating unwanted interference.…”

Investigation of low-density parity check codes concatenated multi-user massive multiple-input multiple-output systems with imperfect channel state information

“…MU-mMIMO follows channel reciprocity due to which the channel state information (CSI) acquired in the uplink can also be used in the downlink mode because of the implementation of time division duplexing (TDD). 4 In Khan et al, 5 the authors have proposed a new scheme of channel estimation. The user does not estimate the channel upon receiving the pilot symbol, but provides feedback to the BS so that the channel’s joint recovery takes place at the BS.…”