Pilot design for sparse MIMO-OFDM channel estimation with generalized shift invariance property

Xiao, Shenyang; Zhang, Jin; Su, Yishan

doi:10.1016/j.aeue.2018.08.028

Cited by 7 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reference [13] proposes a shift mechanism based genetic algorithm (GA) method to design the pilot pattern for a multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system. Two generalized shift mechanism based methods are proposed in [22] for a MIMO-OFDM system.…”

Section: Related Workmentioning

confidence: 99%

Joint Mutual Coherence and Total Coherence Pilot Design for OFDM Channel Estimation

et al. 2019

Self Cite

View full text Add to dashboard Cite

Existing works design the pilot based on the mutual coherence criterion or total coherence criterion alone. However, using the mutual coherence criterion or total coherence criterion alone to design the pilot will yield the sensing matrix which corresponds to a certain pilot with a large total coherence or mutual coherence. However, the sensing matrix with a large mutual coherence or total coherence will deteriorate the sparse recovery performance and is not optimal from the perspective of the sparse recovery. Therefore, in this paper, we jointly design the pilot based on both the mutual coherence criterion and the total coherence criterion for sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. We first prove that the optimal pilot pattern with respect to the total coherence criterion is the cyclic difference set and given a pilot pattern, the pilot power allocation can be cast as a second order cone programming problem with respect to the total coherence criterion. A joint mutual coherence and total coherence pilot design algorithm is proposed to design the pilot jointly while cyclic difference set does not exist for practical OFDM systems. The proposed algorithm first optimizes the pilot pattern with the mutual coherence criterion assuming that the pilot power is equal. After obtaining the pilot pattern, the pilot power is allocated with the total coherence criterion. Simulation results have shown that the proposed pilot design algorithm can achieve the best performance in terms of normalized mean square error and bit error rate in comparison with existing pilot design methods.INDEX TERMS Channel estimation, compressed sensing (CS), pilot design, orthogonal frequency division multiplexing (OFDM).

show abstract

Section: Related Workmentioning

confidence: 99%

Joint Mutual Coherence and Total Coherence Pilot Design for OFDM Channel Estimation

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Section 5 concludes the work. 13 designed inventive patterns by reducing the scale matrix's cross-coherence with GSIP. Then, two practical model design schemes were proposed to create GSIP-based orthogonal experimental patterns for the MIMO-OFDM system: PDGSIP and TPDGSIP.…”

mentioning

confidence: 99%

Binomial distribution based grey wolf optimization algorithm for channel estimation in wireless communication system

Selvaraj,

Shanmugam,

Thangarajan

2023

Int J Communication

View full text Add to dashboard Cite

SummarySeveral input high‐data‐rate transmissions over broadband wireless channels are possible using multiple input multiple output (MIMO) systems paired with orthogonal frequency division multiplexing (OFDM) technology. Channel estimation is an essential technique and a necessary component of MIMO‐OFDM systems. However, the noise will be there in MIMO‐OFDM due to the environment. As a result, the wireless system performs degrades in terms of bit error rate (BER). The suggested method offers a better pilot pattern strategy for MIMO‐OFDM and an efficient power allocation to address this issue. The binomial distribution‐based grey wolf optimization (BDGWO) algorithm is proposed to identify the optimal pilot patterns. The power is then adaptively distributed to each transmit antenna to increase the spectral efficiency and maximum channel capacity through an adaptive neuro‐fuzzy inference system with a sigmoid membership function (SMFANFIS). The best pilot patterns in PDGSIP (pilot design with generalized shift invariant property) were determined using the BDGWO algorithm based on the binomial distribution. According to the simulation results, the proposed BDGWO established pilot design with generalized shift invariant property (BDGWO‐DGSIP) achieves higher performance compared other existing approaches such as PDGSIP, TPDGSIP, and LS in terms of NMSE, BER, and SER. Compared to the PDGSIP technique, the proposed PDGSIP‐BDGWO system minimizes NMSE at 10%, BER at about 12%, and SER at 15%.

show abstract

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

Roji,

Jayasankar,

Nirmala Devi

2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Compressed sensing is used for channel estimation in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems, but large‐scale networks face challenges regarding the antenna elements and spatial non‐stationarities. To enhance spectral efficiency in Multi User‐MIMO (MU‐MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter‐symbol interference rate along with inter‐carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi‐blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

show abstract

Pilot design for sparse MIMO-OFDM channel estimation with generalized shift invariance property

Cited by 7 publications

References 44 publications

Joint Mutual Coherence and Total Coherence Pilot Design for OFDM Channel Estimation

Joint Mutual Coherence and Total Coherence Pilot Design for OFDM Channel Estimation

Binomial distribution based grey wolf optimization algorithm for channel estimation in wireless communication system

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

Contact Info

Product

Resources

About