Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

By Junyoung Nam, Ansuman Adhikary, J. Ahn, Giuseppe Caire. IEEE Journal on Selected Topics in Signal Processing, 2014

358 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/26afb306b0f3b6347382dc33efdd0f8dd4d6ea73

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

5G roadmap: 10 key enabling technologies (2016, background): “The radio access network architecture will be enhanced with the deployment of wireless software-defined networking, network function virtualization, ultra-densification, device-to-device communications, millimeter wave, massive MIMO, and new radio access techniques, while the core network will evolv”
Millimeter Wave Cellular Networks: A MAC Layer Perspective (2015, background): “2) Semi-directional communications: Considering a large number of antennas, with a limited number of RF chains, the BS can group UEs together based on the second order statistics of the channel and serve every group of UEs that have similar covariance matrix with one analog beamforming vector [36], ”
Intelligent Reflecting Surface-Assisted Multi-User MISO Communication: Channel Estimation and Beamforming Design (2020, background): “As discussed in several works, they can be calculated based on knowledge of only the users’ AoAs (which depend on their locations) and angular spread in the environment, both of which can be accurately learned and tracked at the BS [1], [31] 4 .”
Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO (2015, background): “Recent study and experiments have shown that the wireless channels between the BS and users exhibit a small angle spread seen from the BS [4], [16], [17].”
Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (2019, methodology): “limited angular spread [29], so that each UE’s massive MIMO channel exhibits clustered sparsity in the virtual angular domain.”
A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives (2018, neutral): “forming matrix [175].”
Massive MIMO Detection Techniques: A Survey (2019, background): “A plethora of literature can be found on precoding techniques such as the joint spatial division and multiplexing (JSDM) [40], [41], iterative algorithms for precoder optimization [42] and JSDM-ﬁnite alphabets [43].”
Beam Division Multiple Access Transmission for Massive MIMO Communications (2015, background): “To meet these conditions, the authors studied user selection and opportunistic beamforming in [14].”
Joint Spatial Division and Multiplexing for mm-Wave Channels (2013, methodology): “Efficient user grouping algorithms for JSDM are proposed in [21].”
Hybrid Beamforming for Massive MIMO - A Survey (2016, methodology): “Extending the JSDM to a scenario where each individual UE has its own channel covariance, [25] proposes two UE grouping methods: one is the K-means clustering (also see [26]), the other is fixed quantization.”
Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget (2018, methodology): “Several contributions relied on statistical models [8], [46], [67], [76], [82], [102], [176], [177], while other studies were based either on analytical models or on ray tracing techniques under idealized simplifying assumptions [8], [38], [46], [75], [178].”