A User Selection Algorithm Using Angle between Subspaces for Downlink MU-MIMO Systems

Nam, Seongho; Kim, Jeongchan; Han, Youngnam

doi:10.1109/tcomm.2014.010414.130073

Cited by 29 publications

(15 citation statements)

References 22 publications

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“…, [100], [107], [113], [118], [122], [125], [126], [138], [141] [66], [98], [103], [104], [113], [114], [117], [119], [120], [125], [129], [130], [131] [124] [39], [40] -MMSE/SLNR [57], [88], [105], [108], [128] [48], [134], [154], [168], [76], [89], [130] [79] --Adaptive [36], [47], [81], [87], [109], [137] [83] - [77], [93], [94], [132] -Codebook-based [30], [133], [142], [157], [162],…”

Section: A Non-linear Precoding With Full Csitmentioning

confidence: 99%

“…3) The geometrical angle (see [104], [120] and references therein), is a metric similar to the matrix volume ratio that measures the spatial compatibility as a function of the all possible correlation coefficients [cf. (5)] between the basis of two subspaces.…”

Section: Compatibility Between Subspacesmentioning

confidence: 99%

“…1) The principal angle between subspaces [104], [117], [120] measures the relative orientation of the basis of V H k = Span(H k ) regarding the basis of VH k = Span(H k ). Given the bases of V H k and VH k , the principal angle is associated with the largest coefficient of correlation [cf.…”

Section: Compatibility Between Subspacesmentioning

confidence: 99%

“…Flop-based [65], [69], [98], [100], [102], [104], [118], [122], [123], [124] Real/complex Operations [59], [60], [62], [67], [69], [71], [72], [99], [113], [155] O(·) [39], [40], [76], [96], [98], [125], [127], [128], [137], [138], [152], [153], [182] Other Metrics [40], [41], [63], [68], [73], [74], [76], [77], [78], [85], [89], [97], [99], [101], [110], [114], [119],…”

Section: Metric Referencesmentioning

confidence: 99%

See 3 more Smart Citations

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

179

123

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Abstract-Remarkable research activities and major advances have been occurred over the past decade in multiuser multipleinput multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.

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Section: A Non-linear Precoding With Full Csitmentioning

confidence: 99%

Section: Compatibility Between Subspacesmentioning

confidence: 99%

Section: Compatibility Between Subspacesmentioning

confidence: 99%

Section: Metric Referencesmentioning

confidence: 99%

See 2 more Smart Citations

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Castañeda

Silva

Gameiro

et al. 2017

IEEE Commun. Surv. Tutorials

179

123

View full text Add to dashboard Cite

show abstract

“…In the MU-MIMO system, multiple users can use the same frequency simultaneously to receive the data from the BS, and it is necessary to eliminate inter-user interference by precoding. The commonly used precoding methods include dirty paper coding (DPC) and block diagonalization (BD) [29]. We adopt BD in this paper for its lower complexity.…”

Section: A M In the Rank Deficient Environmentmentioning

confidence: 99%

Resource Allocation Algorithm for MU-MIMO Systems With Double-Objective Optimization Under the Existence of the Rank Deficient Channel Matrix

Yan

Bonsu

et al. 2019

IEEE Access

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This paper proposes a double-objective optimization resource allocation algorithm for the multiuser multiple-input/multiple-output (MU-MIMO) system in the general wireless environment and demonstrates the maximum number of simultaneously supportable users and the achievable bit rates of users in the general wireless environment with full rank and rank-deficient channels. The double-objective joint optimization algorithm proposed in this paper simultaneously optimizes energy efficiency and system throughput by user selection and power allocation. On this basis, the proposed algorithm guarantees the different QoS requirements of various services, including rate requirements and delay requirements. INDEX TERMS MU-MIMO, rank deficient, double-objective optimization, QoS guarantee, resource allocation.

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Resource allocation and interference suppression with PCA for multicell MU-MIMO systems

Chih

2019

Wireless Netw

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A User Selection Algorithm Using Angle between Subspaces for Downlink MU-MIMO Systems

Cited by 29 publications

References 22 publications

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

An Overview on Resource Allocation Techniques for Multi-User MIMO Systems

Resource Allocation Algorithm for MU-MIMO Systems With Double-Objective Optimization Under the Existence of the Rank Deficient Channel Matrix

Resource allocation and interference suppression with PCA for multicell MU-MIMO systems

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