Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

By Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen. IEEE Transactions on Signal Processing, 2015

478 citations recorded by the source corpus.

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a nonorthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. In addition, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the nonorthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramér-Rao lower bound of the proposed scheme, which enlightens us to design the nonorthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b36376b53fdbf4fd70f6492fc532e9d9ac92e77b

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Citations in the corpus

Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications (2016, methodology): “An alternative approach to AoD/AoA estimation of mmwave channels is to apply the compressed sensing (CS) techniques in [12]–[14], which have been proposed for sparse multipath estimation in angular domain of massive MIMO channels.”
Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (2019, methodology): “Compared to the conventional multiple measurement vector (MMV) problem [34], where identical pilots would be allocated to all pilot subcarriers, employing different pilot matrices across different pilot subcarriers can improve AUD and CE performance according to DCS theory [27].”
A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model (2017, methodology): “the MSE performances of JSDM obviously deteriorate, whereas the MSE curves from SBEM and the CS-based method [21] are”
Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information (2017, background): “, they share an approximately common support [38].”
Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network (2016, background): “The multipleinput and multiple-output (MIMO) NOMA design for small packet transmission and the multi-user detection for uplink grant-free NOMA systems were investigated in [25] and [26], respectively.”
Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey (2018, methodology): “Along with these, modifications to CE schemes used in conventional systems [60]–[62] such as the widely-adopted/standardized codebook-based channel estimators may be helpful in the search for optimal CE schemes for mmWave massive MIMO systems [54].”