Receive Transformation for Diversity Based MIMO System Under Multi-user Interference and Correlated Fading

2017 ◽  
Vol 96 (1) ◽  
pp. 635-645
Author(s):  
Jui Teng Wang
2020 ◽  
pp. 693-701 ◽  
Author(s):  
Naga Raju Challa ◽  
◽  
Kalapraveen Bagadi

Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput and spectral efficiency in 5G communication networks. Inter-antenna Interference (IAI) and Multi-user Interference (MUI) are two major factors that influence the performance of MU–MIMO system. IAI arises due to closely spaced multiple antennas at each User Terminal (UT), whereas MUI is generated when one UT comes in the vicinity of another UT of the same cellular network. IAI can be mitigated by the use of a pre-coding scheme such as Singular Value Decomposition (SVD) and MUI can be cancelled through efficient Multi-user Detection (MUD) schemes. The highly complex and optimal Maximum Likelihood (ML) detector involves a large number of computations, especially when in massive structures. Therefore, the local search-based algorithm such as Likelihood Ascent Search (LAS) has been found to be a better alternative for mitigation of MUI, as it results in near optimal performance using lesser number of matrix computations. Most of the literature have been aimed at mitigating either IAI or MUI, whereas the proposed work presents SVD pre-coding and LAS MUD to mitigate both IAI and MUI. Simulation results indicate that the proposed scheme can attain near-optimal bit error rate (BER) performance with fewer computations.


2016 ◽  
Vol 25 (05) ◽  
pp. 1650041
Author(s):  
Bruno Felipe Costa ◽  
Taufik Abrão

This contribution proposes a precoder-decoder design aiming to improve the performance of multiple-input–multiple-output (MIMO) detectors under correlated fading channels. The MIMO detection principle namely minimum mean squared error (MMSE) detector is analyzed under such channel condition. The proposed approach deploys the channel state information (CSI) aiming to estimate the level of spatial correlation channel, namely normalized correlation index [Formula: see text] and uses this information to improve the MIMO system performance. Furthermore, the impact of the [Formula: see text] estimation errors on the performance, as well the performance degradation for different levels of correlation have been analyzed and compared with the classical MMSE-MIMO detector operating under uncorrelated channels and perfect channel estimation.


2021 ◽  
Author(s):  
Sultan.F Feisso Meko ◽  
Muluneh Mekonnen Tulu ◽  
Terefe Bahiru Bashu

Abstract Nowadays, wireless communication system plays great roles in our dailyactivities and different improvements are requiring because the number of users increase from time to time. At the same time, users need high throughput and link reliability. The forthcoming generation of wireless communication will have to deal with some core requirements for serving large number of users simultaneously, upholdinghigh throughput for each user, assuring less energy consumption, etc. Inter-user interference has a major impact when a wireless communication link has a large number of users. To maintain a particular desired quality of service, sophisticated transmission mechanisms such as interference cancellation need be implemented. As a result, MU-massive MIMO with extremely huge antenna arrays is recommended. The term ”MU-massive MIMO” refers to a system with hundreds or thousands of antennas servicing tens of thousands of customers.Inter-user interference was greatly decreased once the channel vectors were closely orthogonal. As a result, high data rates can be supplied to multiple users at the same time. In this work, researcher investigated performance evaluation of a MU-massive MIMO utilizing different precoding schemes (like, MMSE, ZF, MRT) over nakagami-m fading channel with CSI at base station and users’ terminal. In addition, the researcher analyzed the outcome of pilot reuse factors and shaping (m) parameter.


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