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Massive multiple–input multiple–output (MIMO) systems
Multiple–input multiple–output (MIMO) technology consists of the use of multiple transmit (TX) and receive (RX) antennas for wireless communications. With such technology, additional degrees of freedom become available in the spatial dimension thanks to the antenna arrays. Through exploitation of spatial diversity [11] or spatial multiplexing [12], it is possible to significantly improve signal–to–noise ratio (SNR) and/or achieve better overall spectral efficiency [13]. Initially used in Wi–Fi systems, MIMO technology has gained a lot of attention in recent years. There were initially two modes in which MIMO technology were envisioned: single– user MIMO (SU–MIMO) and multi–user MIMO (MU–MIMO).In conventional SU–MIMO a base station (BS), equipped with multiple antennas, transmits all its data streams to a single user mobile station (MS) in a given time slot. Besides only serving one user at a time, another downside of this approach is that it requires that the MS support MIMO technology and have multiple antennas as well. This leads to increased device cost and size and also demands more processing resources. MU–MIMO, however, enables the BS to transmit its data streams to different users at the same time. The main advantage of this approach is that many users can be served simultaneously, thereby contributing to a better overall network efficiency. Additionally, although support of MIMO technology is required at the MS, the latter does not necessarily have to be equipped with multiple antennas. This technique is thus more flexible with regards to device cost and size.
Nevertheless, in its original form where roughly as many BS antennas as user MSs were targeted, MU–MIMO is not scalable because of the requirements regarding the knowledge of the channel state information (CSI) at both ends. To alleviate this issue, the concept of massive MIMO, which consists of equipping the BS with a very large number of antennas, say 100, serving a fairly smaller number of user MSs simultaneously, began to gather significant interest as discussed in [14], [15] and [16]. This technology leverages time division duplexing (TDD), where only the uplink CSI is estimated. Therefore, the benefits of conventional MIMO can be scaled up with respect to the number of BS antennas and, thereby, help achieve the mobile broadband experience envisioned with 5G systems. In fact, with knowledge of the downlink channels via uplink training, massive MIMO enables beamforming i.e. the ability to focus the radio signal in desired directions thanks to precoding techniques. This concept is illustrated in Fig. I.2.1.
Small–cells network densification
Traditionally, macro–cell BSs are mounted on high towers. They are geared to serve a number of terminals within their coverage areas. These macro–cell BSs have been at the center of the evolution of cellular networks. With the looming capacity challenge in the horizon of 5G, further BS deployment is necessary in order to improve the connectivity experience of the end–users. However, this is not a workable solution for macro–cells for numerous reasons including high operational and maintenance costs, lack of available sites and high output power. To circumvent this bottleneck, the concept of network densification by means of small–cells, illustrated in Fig. I.2.3, have gained huge momentum in recent years [32].
Full–duplex communications
Traditionally, it has been assumed that radio units cannot receive and transmit on the same frequency resource at the same time due to self–interference. Transmission and reception are either alternated via time division, or they occur in different frequency resources. This is referred to as half–duplex (HD) communications. However, recent research studies have started to invalidate this belief. Studies in [49], [50], and [51] have claimed the feasibility of simultaneous two–way communications, termed as full–duplex (FD) communications, by means of interference cancelation techniques such as antenna cancellation.
Table of contents :
Acronyms
Introduction
I. On the Next Generation (5G) Mobile and Wireless Systems
1. Vision and Requirements for 5G
1.1. Overview on 5G technology
1.2. Main 5G requirements
2. Key enabling technologies for 5G
2.1. Massive multiple–input multiple–output (MIMO) systems
2.2. Millimeter–Wave (mm–Wave) communications systems
2.3. Small–cells network densification
2.4. Cloud–based radio access network (C–RAN)
2.5. Network function virtualization (NFV)
2.6. Full–duplex communications
2.7. Device–to–device communications
3. Focus on millimeter–Wave (mm–Wave) technology
II. Millimeter–Wave Channel Modeling and Characterization for 5G
1. Review of earlier channel models
1.1. 3GPP channel models
1.2. WINNER channel models
1.3. COST channel models
1.4. IEEE 802.11ad channel model
1.5. Shortcomings of early channel models with regards to 5G
2. Channel modeling for 5G systems
2.1. METIS channel models
2.2. MiWEBA channel model
2.3. MmMAGIC channel model
2.4. 3GPP channel models
2.5. Parallel channel modeling works
3. Channel measurements for 5G mm–Wave
3.1. Channel large–scale parameters (LSPs) definition
3.2. Outdoor–to–indoor (O2I) propagation scenarios
3.3. Urban outdoor propagation scenarios
4. Motivations for further studies
III. Theoretical Characterization of the Wireless Propagation Channel
1. Geometrical modeling approach
1.1. Multi–path radio propagation channel
1.2. Geometric channel model
2. Frequency–dependence of the propagation channel
2.1. Free–space phenomenon
2.2. Reflection and transmission phenomena
2.3. Diffraction phenomenon
3. Limitations of the theoretical approach
IV. Outdoor–to–Indoor (O2I) Propagation Channel Measurements
1. Outdoor–to–Indoor (O2I) measurement campaign
1.1. Measurement scenario description
1.2. Measurement setups
1.3. Measurement procedures
2. Measurement data processing
2.1. Data processing for setup S1
2.2. Data processing for setup S2 and setup S3
3. Result analysis
3.1. Measurement validation
3.2. Building penetration losses (PELs)
3.3. Channel delay spread (DS)
4. Summary
V. Urban Outdoor Propagation Channel Measurements
1. Urban Outdoor measurement campaign
1.1. Measurement scenario description
1.2. Measurement setups
1.3. Measurement procedure
2. Measurement data processing
2.1. Omnidirectional power delay profiles (PDPs)
2.2. Directional power delay profiles
2.3. Atmospheric oxygen attenuation
3. Result analysis
3.1. Channel delay spread (DS)
3.2. Azimuth–delay power profile (ADPP)
3.3. Channel azimuth spread (AS)
3.4. Propagation path–loss (PL)
4. Summary
Conclusion
A. Channel Measurement Campaigns and Results for 5G
B. Outdoor–to–Indoor (O2I) Measurement Results in Belfort
C. Urban Outdoor Measurement Results in Belfort
1. Delay spread (DS) and azimuth spread (AS) values
2. Azimuth–delay power profiles (ADPPs)
Publications
References
