OFDM Channel Prediction Using Fuzzy Update LMS Algorithm in Time-Variant Mobile Channel

Wen, Jyh‐Horng; Chang, Cheih-Yao; Lee, Gwo-Ruey; Huang, Chao‐Wei

doi:10.1109/vtcf.2006.404

Cited by 14 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fuzzy systems can be used to update LMS algorithm for OFDM channel estimation in time-variant mobile channels. Such a work is reported in [41]. 11.…”

Section: Application Of Fuzzy Fuzzy-neural and Neuro-fuzzy Systems Imentioning

confidence: 82%

Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

View full text Add to dashboard Cite

The proliferation of number of users in a limited wireless spectrum have raised the levels of inter symbol interference (ISI) and have also contributed towards probable degradation of quality of service (QoS). The key challenges faced by upcoming wireless communication systems is to provide high-data-rate wireless access with better QoS. Also, the fast shrinking spectrum for such communication have necessitated the development of methods to increase spectral efficiency. Multiple input multiple output (MIMO) wireless technology is a viable option in such a situation and is likely to be able to meet the demands of these ever-expanding mobile networks. Many researchers have explored this field over a considerable period of time. A sizable portion of the research have been on the application of traditional statistical methods in such areas. Over the years, soft computational tools like artificial neural network (ANN), fuzzy systems and their combinations have received attention in the diverse segments of wireless communication. This is because of the fact that these are learning based systems. These learn from the environment, retain the knowledge and use it subsequently. This paper highlights some of the important application areas in wireless communication which have reported the use of soft computing tools in wireless communication that are in circulation in open literature.

show abstract

“…Fuzzy systems can be used to update LMS algorithm for OFDM channel estimation in time-variant mobile channels. Such a work is reported in [41]. 11.…”

Section: Application Of Fuzzy Fuzzy-neural and Neuro-fuzzy Systems Imentioning

confidence: 82%

Application of Soft Computing Tools in Wireless Communication—A Review

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

View full text Add to dashboard Cite

show abstract

“…Generative adversarial network (GAN) [315][316][317][318], general regression neural network (GRNN) [319,320], and fuzzy neural network (FNN) [321,322] have also been investigated in the channel estimation subject. Likewise, the least mean error [323], metalearning [324], k-means clustering [325], and LS [326] techniques were applied to leverage NN training.…”

Section: Other Neural Networkmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

show abstract

“…Considera-se ainda que um prefixo cíclico foi inserido em cada símbolo OFDM de forma a eliminar a interferência intersimbólica. Neste caso, o símbolo transmitido e o símbolo recebido em cada subportadora estão relacionados por meio de [4], [5]:…”

Section: Modelo Do Sistema Ofdmunclassified

“…O desempenho do preditor propostoé comparado com os algoritmos NLMS (normalized least mean square) e RLS (recursive least squares) por meio de simulações em um cenário sob as especificações do downlink do padrão 3GPP LTE. Diferentemente de outros preditores propostos na literatura [4], [5], [6], nãoé necessário o conhecimento das matrizes de correlação do canal ou uma escolha rígida para o passo de adaptação. Além disso, por utilizar o conceito de set-membership, o preditor apresenta boa velocidade de convergência e reduzida taxa de atualização de seus coeficientes devidoà sua natureza seletiva.…”

Section: Introductionunclassified

Predição de Canal em Sistemas OFDM Utilizando o Algoritmo Set-Membership Affine Projection

Leite¹,

Carvalho²,

Vieira³

2009

Anais Do XXVII Simpósio Brasileiro De Telecomunicações

View full text Add to dashboard Cite

Resumo-A fim de aproveitar as vantagens oferecidas por esquemas adaptativos de transmissão e sistemas que utilizam múltiplas antenas,é necessário dispor de informação atualizada sobre o estado do canal no transmissor e/ou no receptor. Devido a restrições do sistema, como atraso no canal de retorno, eà natureza variante no tempo do canal rádio móvel, a informação sobre o estado do canal pode estar desatualizada, o que conduz a degradação no desempenho do sistema e perda de capacidade. Técnicas de predição da resposta do canal podem prover informação atualizada sobre o estado do canal e, consequentemente, reduzir os impactos negativos causados pela informação desatualizada. Este trabalho apresenta um preditor adaptivo de canal aplicável a sistemas OFDM baseado no conceito de filtragem set-membership affine projection. Um modelo realista de canal, padronizado pelo 3GPP (Third Generation Partnership Project),é utilizado para avaliar o desempenho do preditor proposto e compará-lo com outros algoritmos adaptativos clássicos, a citar NLMS e RLS.É também mostrado o desempenho do preditor sob o ponto de vista da taxa de erro de bit em um cenário sob as especificações do downlink do padrão 3GPP Long Term Evolution (LTE). Palavras-Chave-Canais variantes no tempo, filtros adaptativos, LTE, OFDM, predição de canal.

show abstract

OFDM Channel Prediction Using Fuzzy Update LMS Algorithm in Time-Variant Mobile Channel

Cited by 14 publications

References 7 publications

Application of Soft Computing Tools in Wireless Communication—A Review

Application of Soft Computing Tools in Wireless Communication—A Review

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Predição de Canal em Sistemas OFDM Utilizando o Algoritmo Set-Membership Affine Projection

Contact Info

Product

Resources

About