12605

11532

كارشناسي ارشد

مخابرات

اصفهان: دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر

1396

يازده، 87ص.: مصور، جدول، نمودار

ص. ع. به فارسي و انگليسي

محمد صادق فاضل، مجتبي بهشتي

محمد جواد اميدي، محمد مهدي نقش

1396/05/04

كتابنامه

برق و كامپيوتر

مهندسي برق و كامپيوتر

ID11532

Doubly Selective Channel Estimation in OFDM based Massive MIMO Systems Using Compressed Sensing Esmaeil Karani e karani@ec iut ac ir JUNE 21 2017 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisors Dr Mohammad Sadegh Fazel fazel@cc iut ac ir Dr Mojtaba Beheshti behesht@cc iut ac ir Abstract Massive MIMO is considered as one of the promising technologies that can meet the growing demands of 5G commu nications It has the potential to provide substantial enhancements in both link reliability and data throughput of the system In order to achieve the advantages of massive MIMO the base station requires accurate channel impulse response CIR foreach transmit receive link Most existing works consider the frequency selective FS channels with slow time variations However high mobility communications have been incorporated as an essential part of the 5G communications Therefore there is need to develop new channel estimation methods for communication over channels which are both FS and time selective or doubly selective DS DS channel estimation is extremely challenging because the parameters to be estimatedare numerous In this thesis the DS channel estimation for Massive MIMO OFDM systems is discussed In contrast to theexisting literature it is assumed that the channel varies within each OFDM block The DS channel is modeled using basisexpansion model BEM By using BEM the number of parameters to be estimated is considerably decreased EstimatingBEM coefficients using linear estimators itself still needs transmitting substantial number of pilots which is not suitablein terms of users sum rate On the other hand it is known that most wireless channels can be modeled as discrete multipathchannels with large delay spread and very few significant paths This results in sparse CIR and sparse BEM coefficients In this thesis novel methods are proposed to estimate the BEM coefficients using compressed sensing CS and block CSrecovery tools The simulation results show that the proposed CS and block CS based fewer number of pilots Key Words Massive MIMO OFDM Doubly Selective Channel Compressed Sensing ChannelEstimation

محمد صادق فاضل، مجتبي بهشتي

محمد جواد اميدي، محمد مهدي نقش