scholarly journals Emulating Realistic Bidirectional Spatial Channels for MIMO OTA Testing

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Fan ◽  
Pekka Kyösti ◽  
Jesper Ø. Nielsen ◽  
Lassi Hentilä ◽  
Gert F. Pedersen
Keyword(s):  
Communication Systems ◽  
Experimental Validation ◽  
Multiple Input Multiple Output ◽  
Line Of Sight ◽  
Channel Models ◽  
Bidirectional Communication ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Time Division ◽  
Space Line

This paper discusses over the air (OTA) testing for multiple input multiple output (MIMO) capable terminals with emphasis on modeling bidirectional spatial channel models in multiprobe anechoic chamber (MPAC) setups. In the literature, work on this topic has been mainly focused on how to emulate downlink channel models, whereas uplink channel is often modeled as free space line-of-sight channel without fading. Modeling realistic bidirectional (i.e., both uplink and downlink) propagation environments is essential to evaluate any bidirectional communication systems. There have been works stressing the importance of emulating full bidirectional channel and proposing possible directions to implement uplink channels in the literature. Nevertheless, there is no currently published work reporting an experimental validation of such concepts. In this paper, a general framework to emulate bidirectional channels for time division duplexing (TDD) and frequency division duplexing (FDD) communication systems is proposed. The proposed technique works for MPAC setups with arbitrary uplink and downlink probe configurations, that is, possibly different probe configurations (e.g., number of probes or their configurations) in the uplink and downlink. The simulation results are further supported by measurements in a practical MPAC setup. The proposed algorithm is shown to be a valid method to emulate bidirectional spatial channel models.

PROBLEM STATEMENT OF JOINT ANTENNA SELECTION AND PRECODING IN MASSIVE MIMO COMMUNICATION SYSTEMS

2021 ◽  
Author(s):  
В.Б. КРЕЙНДЕЛИН ◽  
М.В. ГОЛУБЕВ
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Legal Restrictions ◽  
Multiple Transmission

Совместный с прекодингом автовыбор антенн на приемной и передающей стороне - одно из перспективных направлений исследований для реализации технологий Multiple Transmission and Reception Points (Multi-TRP, множество точек передачи и приема) в системах со многими передающими и приемными антеннами Massive MIMO (Multiple-Input-Multiple-Output), которые активно развиваются в стандарте 5G. Проанализированы законодательные ограничения, влияющие на применимость технологий Massive MIMO, и специфика реализации разрабатываемого алгоритма в миллиметровомдиапа -зоне длин волн. Рассмотрены алгоритмы формирования матриц автовыбора антенн как на передающей, так и на приемной стороне. Сформулирована строгая математическая постановка задачи для двух критериев работы алгоритма: максимизация взаимной информации и минимизация среднеквадратичной ошибки. Joint precoding and antenna selection both on transmitter and receiver sides is one of the promising research areas for evolving toward the Multiple Transmission and Reception Points (Multi-TRP) concept in Massive MIMO systems. This technology is under active development in the coming 5G 3GPP releases. We analyze legal restrictions for the implementation of 5G Massive MIMO technologies in Russia and the specifics of the implementation of the developed algorithm in the millimeter wavelength range. Algorithms of antenna auto-selection matrices formation on both transmitting and receiving sides are considered. Two criteria are used for joint antenna selection and precoding: maximizing mutual information and minimizing mean square error.

scholarly journals Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

Lattice Reduction Algorithm Based Uplink MC-CDMA

2015 ◽  
Vol 738-739 ◽  
pp. 391-396
Author(s):  
Umut Yunus ◽  
Askar Hamdulla ◽  
Zhen Hong Jia ◽  
Kurban Ubul
Keyword(s):  
Communication Systems ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Lattice Reduction ◽  
Rate Performance ◽  
Multiple Input ◽  
High Spectral Efficiency ◽  
Input Multiple Output ◽  
Cdma Systems ◽  
Multi Access

MC-CDMA integrates the advantages of OFDM with those of CDMA, it has high spectral efficiency, robustness against multi-path propagation and multiple access flexibility. Due to the above mentioned merits, it has been considered as a candidate for future wireless. In recent years, lattice reduction technique is discussed in multiple input multiple output communication systems, and has been shown with its better performance. The purpose of this paper is to express a model for uplink MC-CDMA systems in matrix form and then to propose a lattice reduction aided multiuser detection, in order to ameliorate the affects of inter-carrier interference and multi access interference. The effectiveness of the proposed method is evaluated by the bit error rate performance.

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