Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO (1512.02918v3)

Abstract: Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the non-orthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multi-cell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

• The paper presents a novel structured compressive sensing approach that leverages spatio-temporal sparsity to significantly reduce pilot overhead in FDD massive MIMO systems.
• It introduces an adaptive structured subspace pursuit algorithm at the user equipment that enables joint channel estimation across multiple OFDM symbols with near-optimal accuracy.
• Simulation results validate that the proposed space-time adaptive pilot scheme effectively mitigates pilot contamination in multi-cell environments while aligning with next-generation 5G requirements.

Structured Compressive Sensing for Channel Estimation in FDD Massive MIMO

The paper "Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO" by Zhen Gao et al. presents a novel approach for channel estimation to address the increasing challenges posed by frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, a crucial technology for future 5G communication networks. The authors propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme that effectively reduces the pilot overhead while maintaining accurate channel estimation, leveraging the inherent spatio-temporal sparsity of the MIMO channels. This approach is pivotal due to the impracticality of pilot overhead in conventional estimation methods in the context of massive MIMO systems with hundreds of antennas.

The authors introduce a non-orthogonal pilot scheme at the base station (BS) within the compressive sensing (CS) framework that significantly reduces the pilot overhead compared to traditional orthogonal pilot schemes. This innovative pilot design capitalizes on the compressed sensing theory to facilitate the capture of the sparse nature of the delay-domain channels, which is unfeasible with classical Nyquist sampling theorems.

At the user equipment (UE), an adaptive structured subspace pursuit (ASSP) algorithm is advanced to enhance channel estimation. The ASSP algorithm allows for the joint estimation of channels connected with multiple orthogonal frequency-division multiplexing (OFDM) symbols using a limited number of pilots, exploiting the spatial and temporal common sparsity effectively to improve the channel estimation accuracy.

Furthermore, the authors present a space-time adaptive pilot scheme, which, by leveraging temporal channel correlation, further diminishes pilot overhead. This is crucial as it enables compatibility with existing frequency division-based networks, a necessity as current cellular systems largely operate on such schemes versus time division duplex (TDD) systems. The geographical arrangement of antenna arrays and user mobility are considered to optimize this scheme.

The authors extend their proposed estimation scheme to multi-cell environments, considering pilot contamination — a critical challenge in multi-cell systems. They propose employing frequency-division multiplexing (FDM) and time-division multiplexing (TDM) to mitigate inter-cell interference. The paper underscores the applicability of the proposed techniques using simulation results, which demonstrate that the SCS-based scheme can accurately approach the performance of the optimal oracle least squares estimator with a significantly reduced pilot overhead — an important observation for the practical deployment and operation of FDD massive MIMO systems.

Key implications of this research point to the viability of applying structured compressive sensing methodologies to channel estimation problems in next-generation massive MIMO networks. Theoretical and practical advantages are clearly visible in pilot overhead reduction, operation efficiency, and resource allocation, emphasizing their importance in deploying future 5G networks under frequency division protocols.

Overall, while challenges remain in fully realizing the potential of massive MIMO in real-world deployments, approaches like the one discussed in this paper demonstrate tangible steps toward mitigating such challenges. Future research can build on these foundations to refine algorithms for more dynamic and environment-aware adaptations, ultimately supporting the nuanced requirements of next-generation communication networks.