Graph Theory Based Approach to Users Grouping and Downlink Scheduling in FDD Massive MIMO

Abstract

Massive MIMO is considered as one of the key enablers of the next generation 5G networks. With a high number of antennas at the BS, both spectral and energy efficiencies can be improved. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antenna. This does not create complications in Time Division Duplex (TDD) systems since the channel estimate of the uplink direction can be directly utilized for link adaptation in the downlink direction. However, this channel reciprocity is unfeasible for the Frequency Division Duplex (FDD) systems where different physical transmission channels are existent for the uplink and downlink. In the aim of reducing the amount of Channel State Information (CSI) feedback for FDD systems, the promising method of two stage beamforming transmission was introduced. The performance of this transmission scheme is however highly influenced by the users grouping and selection mechanisms. In this paper, we first introduce a new similarity measure coupled with a novel clustering technique to achieve the appropriate users partitioning. We also use graph theory to develop a low complexity groups scheduling scheme that outperforms currently existing methods in both sum-rate and throughput fairness. This performance gain is demonstrated through computer simulations.

Evaluation of the Spatial Consistency Feature in the 3GPP Geometry-Based Stochastic Channel Model

“…Thus, large scale parameter independent thresholds can be defined, e.g. in [19] a similarity threshold of CMD = 0.95 is set as a condition for users to be in the same cluster. In our numerical evaluation such a value is approximately achieved at 1m distance for d λ ≥ 15 m. On the other hand, even for d λ = 0 m (e.g.…”

“…Motivated by this, we investigate the two performance metrics based on covariance matrices in this subsection. The first makes use of the chordal distance criterion, the second adapts the Correlation Matrix Distance (CMD) measure introduced in [19]. 1) Chordal distance: The chordal distance has been introduced by [20] and is widely used as a distance metric between subspaces.…”

Exploiting the Massive MIMO Channel Structural Properties for Minimization of Channel Estimation Error and Training Overhead

“…The weight expression of (54) is inspired by the single and weighted average linkage, commonly used in hierarchical clustering [55] and the similarity measure used in [56] and [57]. Note that I (k ,c )→(k,c) can be regarded as a measure of the interference user k of cell c would introduce to the channel estimate of user k of cell c if both users had the same training sequence.…”

On Optimal Scheduling for Joint Spatial Division and Multiplexing Approach in FDD Massive MIMO

“…JSDM [8] revolves around the idea of splitting the precoder V into two stages: V = BP , where B and P are referred to as the outer and inner precoders respectively. The outer precoder B, of dimensions N t × b, is based on the channel statistics which is supposed to be known at the base station as adopted in [8] 1 . The design of the outer precoder can be aimed to minimizing inter-group interference.…”