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Multiuser multiple-input-multiple-output (MU- MIMO) systems are known to be hindered by dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead.
標(biāo)簽： Acquisition Dynamic Channel
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
The writing of this book was prompted by two main developments in wireless communications in the past decade. First is the huge surge of research activities in physical-layer wireless communication theory. While this has been a subject of study since the 60’s, recent developments in the field, such as opportunistic and multi-input multi-output (MIMO) communication techniques, have brought completely new per- spectives on how to communicate over wireless channels.
標(biāo)簽： Communication Fundamentals Wireless of
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation.
標(biāo)簽： Large-scale Antenna Systems
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
In order to improve the spectral efficiency in wireless communications, multiple antennas are employed at both transmitter and receiver sides, where the resulting system is referred to as the multiple-input multiple-output (MIMO) system. In MIMO systems, it is usually requiredto detect signals jointly as multiple signals are transmitted through multiple signal paths between the transmitter and the receiver. This joint detection becomes the MIMO detection.
標(biāo)簽： Complexity Detection MIMO Low
上傳時(shí)間： 2020-05-27
上傳用戶：shancjb
Use of multiple antennas at both ends of wireless links is the result of the natural progression of more than four decades of evolution of adaptive antenna technology. Recent advances have demonstrated that multiple- input-multiple-output (MIMO) wireless systems can achieve impressive increases in overall system performance.
標(biāo)簽： Technology System MIMO
上傳時(shí)間： 2020-05-28
上傳用戶：shancjb
The family of recent wireless standards included the optional employment of Multiple-Input Multiple-Output(MIMO)techniques.This was motivatedby the observationaccordingto the classic Shannon–Hartley law that the achievable channel capacity increases logarithmically with the transmit power. In contrast, the MIMO capacity increases linearly with the number of transmit antennas, provided that the number of receive antennas is equal to the number of transmit antennas. With the further proviso that the total transmit power is increased in proportion to the number of transmit antennas, a linear capacity increase is achieved upon increasing the transmit power, which justifies the spectacular success of MIMO systems.
標(biāo)簽： Multi-Functional Systems MIMO
上傳時(shí)間： 2020-05-31
上傳用戶：shancjb
The purpose of this book is to introduce the concept of the Multiple Input Multiple Output (MIMO) radio channel, which is an intelligent communication method based upon using multiple antennas. The book opens by explaining MIMO in layman’s terms to help stu- dents and people in industry working in related areas become easily familiarised with the concept. Therefore the structure of the book will be carefully arranged to allow a user to progress steadily through the chapters and understand the fundamental and mathematical principles behind MIMO through the visual and explanatory way in which they will be written. It is the intention that several references will also be provided, leading to further reading in this highly researched technology.
標(biāo)簽： Practical Guide MIMO to
上傳時(shí)間： 2020-05-31
上傳用戶：shancjb
The multiple-input multiple-output (MIMO) technique provides higher bit rates and better reliability in wireless systems. The efficient design of RF transceivers has a vital impact on the implementation of this technique. This first book is com- pletely devoted to RF transceiver design for MIMO communications. The book covers the most recent research in practical design and applications and can be an important resource for graduate students, wireless designers, and practical engineers.
標(biāo)簽： Transceiver Design RF
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
Driven by the desire to boost the quality of service of wireless systems closer to that afforded by wireline systems, space-time processing for multiple-input multiple-output (MIMO) wireless communications research has drawn remarkable interest in recent years. Excit- ing theoretical advances, complemented by rapid transition of research results to industry products and services, have created a vibrant and growing area that is already established by all counts. This offers a good opportunity to reflect on key developments in the area during the past decade and also outline emerging trends.
標(biāo)簽： Space-Time Processing
上傳時(shí)間： 2020-06-01
上傳用戶：shancjb
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convo- lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion.
標(biāo)簽： Convolutional Connected Networks Densely
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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