Decentralized User Scheduling for Rate-Constrained Sum-Utility Maximization in the MIMO IBC

Antonioli, Roberto P.; Fodor, Gábor; Soldati, Pablo; Maciel, Tarcísio F.

doi:10.1109/tcomm.2020.3009369

Cited by 5 publications

(2 citation statements)

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“…Other works, such as [20], [21], have focused on the sum-energy efficiency maximization problem, that consists in minimizing the energy consumption per transmitted bit. The weighted sum-rate maximization problem with QoS guarantees was considered in [22]- [24]. In [22] the authors proposed centralized and distributed solutions based on successive convex approximation (SCA), difference of convex functions program (DCP) and Lagrangian relaxation.…”

Section: Trafficmentioning

confidence: 99%

“…In [22] the authors proposed centralized and distributed solutions based on successive convex approximation (SCA), difference of convex functions program (DCP) and Lagrangian relaxation. Centralized, semidistributed and distributed solutions were proposed in [23], [24] based on the BB method, geometric programming and second-order cone programming (SOCP), SCA and DCP.…”

Section: Trafficmentioning

confidence: 99%

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Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization under Latency Constraints in Multicell MU-MIMO Systems

Braga¹,

Antonioli²,

Fodor³

et al. 2021

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Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems.Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm.Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model.

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