LS Estimation Algorithm Research Articles

Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

In this paper, the optimality of Compressive Sensing based Least Square (LS-CS) and Compressive Sensing based Minimum Mean Square (MMSE-CS) channel estimation algorithms in Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is investigated for a sparse communication channel. The performance of LS, MMSE, LS-CS and MMSE-CS channel estimation algorithms in terms of sparsity of the channel, compressive sensing and mathematical complexity is investigated and analyzed so that optimum ranges can be recommended.

Channel Estimation and Data Transmission in Massive MIMO TDD Systems

This paper mainly elaborates the studies of channel estimation and downlink data transmission in Massive MIMO. As there are different types of interference in single-cell and multi-cell systems, this paper establishes different models for them separately. In terms of uplink training, for getting channel state information, we introduce LS and MMSE channel estimation algorithms and make a comparison between them. At the same time, the problem of pilot contamination is solved by cell classification and pilot identification. Next, this paper defines mathematical models for downlink data transmission. We use pre-coding methods (including Zero-forcing and Maximal Ratio Combining schemes) and optimize power distribution to improve channel capacity and transmission rate. Furthermore, this paper provides numerical results to show the simulation performance in both single-cell and multi-cell systems and extends to prospects in the future.

Performance Comparison of LS and LMMSE Channel Estimation Algorithm for CO-OFDM System

The CO-OFDM single-mode optical fiber system is constructed by the Matlab coupled with the Optisystem. And the influence of different channel algorithm on the receiver of system is researched. The simulation results show that the system performance is improved by using LS or LMMSE channel algorithm. And the LMMSE channel algorithm can provide better performance than the LS channel algorithm. Compared with LS algorithm, the required receiver OSNR is reduced by about 1 dB using improved LMMSE algorithm at bit-error-rate (BER) =10-4 in the CO-OFDM single-mode optical fiber system.

Order-recursive Least-squares Adaptive Algorithms — a Unified Framework

This paper provides a unified framwork for existing least-sqaares adaptive filtering and estimation algorithms that are both time-and order-recursive (TORLS). It is shown that these algorithms can he derived by the decomposition principle of LS estimation. They can be all realized using only two types of basic processing cells each of which implements a simple set of sealar operations. Thus the TORLS algorithms are suitable for systolic array implementation. We also show that the various existing TORLS algorithms can be investigated by exploring the variations of these basic cells and three TORLS algorithm structures. By duing su, we reveal the relationship between the LS estimation algorithm based on Givens rotation and other TORLS algorithms including the LS lattice algorithm. A new LS lattice algorithm based on Givens rotations is derived in this paper

A recursive modified Gram-Schmidt algorithm for least- squares estimation

This paper presents a recursive form of the modified Gram-Schmidt algorithm (RMGS). This new recursive least-squares (RLS) estimation algorithm has a computational complexity similar to the conventional RLS algorithm, but is more robust to roundoff errors and has a highly modular structure, suitable for VLSI implementation. Its properties and features are discussed and compared to other LS estimation algorithms.