Enhanced Subset Greedy Multiuser Scheduling in Clustered Cell-Free Massive MIMO Systems

Abstract

In this work, we consider the problem of multiuser scheduling for the downlink of cell-free massive multi-input multi-output networks with clustering. In particular, we develop a multiuser scheduling algorithm based on an enhanced greedy method that is deployed with linear precoding and clustering. Closed-form expressions for the sum-rate performance are derived when imperfect channel state information is considered. The proposed scheduling algorithm is then analyzed along with its computational cost and network signaling load. Numerical results show that the proposed scheduling method outperforms the existing methods and in low signal-to-noise ratios, its performance becomes much closer to the optimal approach.

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

“…As the number of iterations increases, there is more a posterior information about the transmitted bit. This implies that mean of the interference symbol si ≈ s i in (8). Thus, the filter becomes a perfect interference canceler, and ( 12) yields…”

“…The receiver consists of an MMSE filter followed by a soft interference cancellation scheme, which may use either a successive or a list-based successive interference cancellation technique. The receiver first creates soft estimates of the transmitted symbols by computing the jth UE symbol mean sj based on the soft beliefs from the LDPC decoder [13], [32], [33] given E {s j } = sj as described by sj = s∈A sP(s j = s), (8) where A is the set of complex constellations. The a-priori probability of the extrinsic LLRs is given by [32], [33]…”

“…In the first iteration it is considered that si = 0 in (8). In this case, we have a linear MMSE filter and the estimated signal in ( 12) is…”

“…In the first iteration, si = 0 in (8), which yields the linear MMSE filter and the estimated signal in (25) given by…”

Multiuser Scheduling with Enhanced Greedy Techniques for Multicell and Cell-Free Massive MIMO Systems

“…In this paper, we investigate the sum-rate performance of the multicell and cell-free massive MIMO systems using greedy user scheduling based on ZFS algorithm in the downlink 1 . We consider the use of a network-centric clustering approach to reduce the computational complexity of the techniques applied to the cell-free systems [38]. We then develop a greedy algorithm that considers multiple candidates for the subset of users to be scheduled and that approaches the performance of the optimal exhaustive search.…”