A Near Optimal Scheduling Algorithm for Efficient Radio Resource Management in Multi-user MIMO Systems

Naeem, Muddasar; Bashir, Sajid; Ullah, Zaib; Syed, Aqeel A.

doi:10.1007/s11277-019-06222-3

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…L ) of the low-complexity MU-MIMO scheduling algorithm using BER minimization strategy with a fixed rate can be described as (18).…”

Section: B Algorithmmentioning

confidence: 99%

“…In MU-MIMO systems where there are many users in a cell, the transmitter needs to select a subset of the users to provide resources to at any given time. Many low complexity MU-MIMO scheduling algorithms have therefore been proposed because the computational complexity of the optimal (exhaustive search) MU-MIMO scheduling algorithm is prohibitive [12]- [18]. The BD scheme typically eliminates a subspace that includes not only the interference channel space, but also a part of the desired channel space.…”

Section: Introductionmentioning

confidence: 99%

“…Femenias et al considered MU-MIMO scheduling and resource allocation schemes in orthogonal frequency division multiple access (OFDMA) systems, including subcarrier, user, and stream selections [16], and Pattanayak et al proposed user scheduling and antenna selection schemes with low complexities [17]. On the other hand, an MU-MIMO scheduling algorithm handling both sum-rate and fairness simultaneously were considered in [18] unlike the conventional scheduling schemes to maximize only sumrate.…”

Section: Introductionmentioning

confidence: 99%

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Joint Power Allocation and Scheduling Techniques for BER Minimization in Multiuser MIMO Systems

Byun

Lee

et al. 2021

IEEE Access

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In contrast to the 4G LTE, one of the key requirements of 5G and beyond networks is ultrahigh reliability. Towards this end, we investigate the problem of scheduling with joint power allocation for multi-user multiple-input multiple-output (MU-MIMO) systems by taking the reliability into account. In this study, two novel MU-MIMO scheduling methods are proposed based on the use of bit error rate (BER) as the scheduling metric; however, capacity has been typically considered as an user-selection metric in MU-MIMO systems to increase the sum-throughput. In particular, we also develop a new power allocation strategy that can minimize the BER for a given data transmission rate. The computational complexities of the two aforementioned BER-based scheduling algorithms, as well as the conventional capacity-based algorithm, are analyzed. Our simulation results show that the proposed BER-based scheduling algorithms with the joint power allocation have better performance in terms of BER compared to the conventional MIMO scheduling schemes such as a single-user MIMO (SU-MIMO) and the capacity-based scheduling algorithm. The proposed multi-user scheduling framework can be utilized in various emerging applications demanding higher reliability and availability such as mission-critical services.INDEX TERMS Multi-user multiple-input multiple-output (MU-MIMO), bit-error rate (BER), power allocation, scheduling, block diagonalization

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“…L ) of the low-complexity MU-MIMO scheduling algorithm using BER minimization strategy with a fixed rate can be described as (18).…”

Section: B Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Power Allocation and Scheduling Techniques for BER Minimization in Multiuser MIMO Systems

Byun

Lee

et al. 2021

IEEE Access

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“…Efficient user scheduling in multi-user Multiple Input Multiple Output (MIMO) and massive MIMO systems are very important for better utilization of available resources at base station [388], [389], [390] and [391]. The RL policy gradient algorithms studied in [392], are used for user scheduling in [393] and for resource management of computer systems in [394] respectively and shows superior performance.…”

Section: H Scheduling Management and Configuration Of Resourcesmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

Self Cite

135

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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“…More attractively, combining with channel coding techniques and linear signal processing technologys, e.g., Zero-Forcing (ZF) and Minimum-Mean-Square-Error (MMSE) can further enhance the performance of massive MIMO. With the appropriate user scheduling scheme [2], all communicating entities can be obtained an equal opportunity without sacrificing sum-rate performance. For MU-MIMO uplink orthogonal frequency division multiple access (OFDMA) video transmission systems, the authors of [3] propose an average peak-signal-to-noise-ratio (PSNR) optimized cross-layer resource allocation and user grouping scheme, in which zero-forcing MU-MIMO detector is considered.…”

Section: Introductionmentioning

confidence: 99%

Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers

Zhai

Lei

2019

Sensors

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In this paper, we estimate the uplink performance of large-scale multi-user multiple-input multiple-output (MIMO) networks. By applying minimum-mean-square-error (MMSE) detection, a novel statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for any user is derived, for path loss, shadowing and Rayleigh fading. Suppose that the channel state information is perfectly known at the base station. Then, we derive the analytical expressions for the pairwise error probability (PEP) of the massive multiuser MMSE–MIMO systems, based on which we further obtain the upper bound of the bit error rate (BER). The analytical results are validated successfully through simulations for all cases.

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A Near Optimal Scheduling Algorithm for Efficient Radio Resource Management in Multi-user MIMO Systems

Cited by 9 publications

References 23 publications

Joint Power Allocation and Scheduling Techniques for BER Minimization in Multiuser MIMO Systems

Joint Power Allocation and Scheduling Techniques for BER Minimization in Multiuser MIMO Systems

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers

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