Low complexity beam selection for sparse massive MIMO systems

Valduga, Samuel Tumelero; Deneire, Luc; Almeida, André L. F. de; Maciel, Tarcísio F.; Aparicio-Pardo, Ramón

doi:10.1109/iswcs.2017.8108150

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Valduga et al [94] Proposed beam selection scheme by exploiting the geometric sparsity of the multi-user massive MIMO to mitigate complexity in [94].…”

Section: References Contributions Resultsmentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications.

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“…Valduga et al [94] Proposed beam selection scheme by exploiting the geometric sparsity of the multi-user massive MIMO to mitigate complexity in [94].…”

Section: References Contributions Resultsmentioning

confidence: 99%

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

et al. 2019

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“…We design a new heuristic to simplify the beam selection and power optimization procedure based on Lagrangian relaxation . Furthermore, for completeness, we briefly describe three other simple low‐complexity heuristics …”

Section: Proposed Solutionmentioning

confidence: 99%

“…21 Furthermore, for completeness, we briefly describe three other simple low-complexity heuristics. 23…”

Section: Proposed Solutionmentioning

confidence: 99%

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Low‐complexity heuristics to beam selection and rate adaptation in sparse massive MIMO systems

Valduga

Deneire

Aparicio-Pardo

et al. 2015

Trans Emerging Tel Tech

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In this work, we propose a novel formulation for a precoder design considering practical rate assignment based on the modulation and coding scheme of the long‐term evolution table, beam selection, and power optimization, which exploits the geometric sparsity of the multiuser massive multiple‐input–multiple‐output channel. We consider two different channel models and provide an optimal solution for the joint beam selection and power optimization as well as a heuristic using Lagrangian relaxation. Assuming knowledge of the beamspace channel, the beamspace precoder consists in selecting and optimizing the power of the beams steered to the user equipments (UEs) in order to maximize the signal‐to‐interference‐plus‐noise ratio at the UE. Furthermore, we propose three additional simple heuristics with low complexity. For these three heuristics, we solve the problem in two steps: (1) selection of beams based on the maximal ratio transmission principle and (2) power allocation per UE. Simulation results show that our optimal solution can achieve a better performance than the zero‐forcing beamforming scheme in the high‐sparsity scenarios. Besides, compared to the linear maximal ratio transmission precoder, the proposed low‐complexity heuristics improve the performance under a scenario with channel sparsity.

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SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

et al. 2020

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Beamspace multiple-input multiple-output (MIMO) systems employ beam selection algorithms based on the sparsity of the beamspace channel to reduce the number of radio-frequency chains required in multiuser systems. In this paper, we address the problem of selecting multiple beams to each user. Specifically, we propose a beam selection scheme subject to signal to interference plus noise ratio (SINR) maximization. The proposed beamspace beam selection algorithm is developed in two stages. In the first stage, each user is assigned a non-overlapping beam determined by Kuhn-Munkres assignment algorithm. In the second stage, the selected beams in the previous stage are augmented with additional beams from an M-dominant beam set subject to a sum-rate maximization criterion. The performance of the proposed scheme is verified by simulations, and the results show a near-optimal performance in terms of the achievable sum-rate. Also, a significant improvement in terms of energy efficiency is achieved compared to the conventional beam selection scheme. INDEX TERMS Beamspace, massive MIMO, millimeter wave communications, multiuser interference, beam selection, beamforming.

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Low complexity beam selection for sparse massive MIMO systems

Cited by 6 publications

References 16 publications

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Low‐complexity heuristics to beam selection and rate adaptation in sparse massive MIMO systems

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

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