A Sparsity-Based Adaptive Channel Estimation Algorithm for Massive MIMO Wireless Powered Communication Networks

Abstract

Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.INDEX TERMS Massive MIMO, wireless powered communication networks, sparse channel estimation, sparsity-based adaptive matching pursuit, energy entropy-based order determination, staged adaptive variable step size.

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

“…Similarly, the improper assumption during CS recovery results in either early or late termination of CS recovery algorithms leading to either poor reconstruction or wastage of resources. Some greedy recovery algorithms such as Sparsity adaptive Matching Pursuit (SaMP) and its variants [8]- [13] and Kronecker-based recovery [14], [15] do not necessarily require the knowledge of the sparsity order for recovery. However, the efficiency of such recovery algorithms depends on the 'step size' parameter that is greatly influenced by the knowledge of sparsity order.…”

“…However, these SaMP algorithms require a step size, whose optimal value depends on an unknown sparsity order. An Optimized Adaptive Matching Pursuit (OAMP) algorithm was proposed in [13] which is similar to SaMP except for the energy entropy-based order determination in updating the support. Recently, the deterministic binary block diagonal (DBBD) matrix-based sensing [14] and Kronecker-based recovery [15] have been proposed for acquiring and recovering compressible signals.…”

“…The NRE performance of the proposed method (Composite sensing-KBSOE) is compared with (i) other similar BSM methods for different sparsity order values, and (ii) 2-GMM [17], DBBD-Kronecker [14], [15], AS-SaMP [10], OAMP [13], MCoSaMP [11], SAStOMP [12], and traditional GSM methods for different SNR values.…”

Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

“…These two algorithms work on the principle of identifying a small subset of sparse taps sequentially. Different modified OMP algorithms are also defined in literature . The significant difficulty of this algorithm is the requirement of sparse degree beforehand, which is practically difficult.…”

“…Different modified OMP algorithms are also defined in literature. [60][61][62][63][64] The significant difficulty of this algorithm is the requirement of sparse degree beforehand, which is practically difficult. This limitation was overcome by using sparsity adaptive matching pursuit (SaMP) algorithm.…”