Book
Channel-aware detection and estimation in the massive MIMO regime
| العنوان: | Channel-aware detection and estimation in the massive MIMO regime |
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| المؤلفون: | Ciuonzo D., Rossi P. S., Dey S. |
| المساهمون: | Ciuonzo, D., Rossi, P. S., Dey, S. |
| بيانات النشر: | Institution of Engineering and Technology |
| سنة النشر: | 2019 |
| المجموعة: | IRIS Università degli Studi di Napoli Federico II |
| مصطلحات موضوعية: | Amplify and forward communication, Amplify-and-forward sensor, Asymptotic mse approximation, Binary hypothesis testing, Channel estimation, Channel-aware detection, Channel-aware distributed detection, Channel-aware estimation, Communication channel equalisation and identification, Convex optimization, Convex programming, Correlated random source vector, Correlation theory, Decentralized estimation, Fading channel, Fusion center, Inhomogeneous large-scale fading, Interpolation and function approximation (numerical analysis), Least mean squares method, Linear fusion rule, Log-likelihood ratio, Massive MIMO regime, MIMO communication, Minimum mean square error, MMSE approach, Multiple-input-multiple-output channel, Multiuser detection, Multiuser MIMO, Numerical approximation and analysi, Optimisation technique |
| الوصف: | This chapter investigates channel-aware distributed detection (viz. binary hypothesis testing, HT) and estimation (EST) over a “virtual” and “massive” multiple-input- multiple-output (MIMO) channel at the fusion center (FC), underlining analogies and differences with uplink communication in a multiuser (massive) MIMO setup. The considered scenario takes into account channel estimation and inhomogeneous large-scale fading between the sensors and the FC. In the former case, the aim is the development of (widely) linear fusion rules, as opposed to the unsuitable (optimum) log-likelihood ratio (LLR). In the latter case, the aim is the power allocation design for decentralized estimation of a correlated random source vector with amplify-andforward sensors and an FC adopting a minimum mean square error (MMSE) approach. In both cases, the well-known favorable propagation condition achieved in massive MIMO is exploited. In the HT problem, this greatly simplifies the development of suboptimal rules, whereas for EST problem this allows to obtain an asymptotic MSE approximation, which is then used with convex optimization techniques to solve the optimal sensor power allocation problem in an efficient fashion. |
| نوع الوثيقة: | book part |
| اللغة: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/isbn/9781785615849; info:eu-repo/semantics/altIdentifier/isbn/9781785615856; ispartofbook:Data Fusion in Wireless Sensor Networks; firstpage:131; lastpage:151; numberofpages:21; http://hdl.handle.net/11588/873280; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85118038257 |
| DOI: | 10.1049/PBCE117E_ch6 |
| الاتاحة: | http://hdl.handle.net/11588/873280 https://doi.org/10.1049/PBCE117E_ch6 |
| رقم الانضمام: | edsbas.8F13C165 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1049/PBCE117E_ch6 |
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