User Scheduling for Sum-Rate Maximization Under Minimum Rate Constraints for the MIMO IBC

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

doi:10.1109/lwc.2019.2930255

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…It is worth noting that the fewer antennas activated, the lower the computational complexity and hardware driving cost can be derived [43]. In contrast, one RF chain can support at most one data stream, which should be transmitted to the corresponding user through at least one antenna.…”

Section: System Model and Rate Analysismentioning

confidence: 99%

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

2021

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Massive multiple-input multiple-output (MIMO) technology, mainly equipped with dozens or even hundreds of antennas at transmitter and/or receiver, is one of the most important technologies in 5G/6G era due to its capability of achieving high transmission rate. However, since each transmit antenna typically needs a complete radio frequency (RF) chain, a large number of RF chains need to be installed accordingly, resulting in high economic cost, high hardware complexity, and high power consumption. To resolve these problems, architectures where a small number of RF chains are installed have been proposed in recent years, and an antenna selection technique that activates antennas only as many as the number of RF chains has been envisioned as one of solutions. In this paper, we study the joint antenna and user scheduling problem for the downlink massive MIMO system over time-varying fading channels to maximize the weighted average sum rate while ensuring users' minimum average data rate requirements. To solve the problem, we first develop an opportunistic joint antenna and user scheduling algorithm (OJAUS) using the dual and stochastic subgradient methods, which makes it possible to schedule antennas and users without any underlying distributions of the fading channels. However, it requires solving a joint antenna and user selection (JAUS) problem to maximize the instantaneous weighted sum rate in every time slot. Thus, we additionally develop a simple heuristic JAUS algorithm with low computational complexity, called JAUS-LCC, which is executed in every time slot within OJAUS. Finally, through simulation results, we first show that our JAUS-LCC provides near-optimal performance despite requiring very low computational complexity, and then show that our OJAUS with JAUS-LCC well guarantees given minimum average data rate requirements.INDEX TERMS Antenna selection, multiple-input multiple-output (MIMO), quality-of-service (QoS), timevarying fading channels, user scheduling, weighted sum rate maximization, zero-forcing (ZF) precoding.

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Section: System Model and Rate Analysismentioning

confidence: 99%

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

2021

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“…where E * i is obtained according to U i and ( 15). According to (17) and (18), f i (F , U i , W i ) can be expressed as…”

Section: Mmse Algorithmmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

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Sum‐rate optimization problem is a key issue in a time‐varying multi‐user multi‐input multi‐output (MU‐MIMO) distributed antenna system. Channel precoding matrix is the key technology in the achievable sum‐rate optimization problem. In this paper, two algorithms are proposed to solve the achievable sum‐rate optimization problem for a time‐varying MU‐MIMO distributed antenna system, namely one‐dimensional search algorithm (OSA) and cyclic MMSE (Minimum Mean Square Error) search algorithm (CMSA). In order to design the channel precoding matrix for the achievable sum‐rate optimization, a one‐dimensional search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. For the problem with a large amount of computation in OSA, a cyclic MMSE search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. Simulation results show that the proposed algorithms can effectively improve the sum‐rate compared to other algorithms. Simulation results also show that OSA is superior to CMSA in the sum‐rate, and the channel state information (CSI) coefficient has little effect on OSA and CMSA.

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“…We formulate a utility-based optimization problem to maximize the total user utility derived from the network, i.e., the total user satisfaction, where the concept of user satisfaction relates to meeting the users' quality of service (QoS) demands [17], [16]. Our optimization problem is:…”

Section: Problem Formulationmentioning

confidence: 99%

“…The UDs dropping criterion follows a hotspot UD distribution per LTE sector, where 75% are deployed within the NR coverage area and the other 25% are uniformly deployed within the LTE sector. Each UD j is configured to have one constant bit rate (CBR)-like bearer and can be connected to one BS from each RAT n, thus l j,n = 1, ∀j, n. A UD is considered satisfied if its total throughput is higher than or equal to a throughput requirement of 20 Mbps [17], [16]. The f thru is set to 0.001 [19].…”

Section: Performance Evaluation In 4g-5g Networkmentioning

confidence: 99%

“…We compare our solution with: 1) a baseline framework with the UBA algorithm from [3], the FCA from [8] and the PF RRA algorithm for DC [13]; 2) the joint UBA and RRA solution from [16] with the FCA from [8]; and 3) the solution from [16] with the FCA from [11]. Several key performance indicators (KPIs) are evaluated: 1) overall percentage of satisfied UDs, which is the number of UDs with throughput higher than the minimum throughput requirement over the total number of UDs [11], [16], [17]; 2) percentage of satisfied UDs considering the UDs in DC [11]; 3) percentage of satisfied UDs considering the UDs in SC [11]; 4) total cell throughput [11], [16], [17]; 5) percentage of UDs in SC [11], [16]; and 6) the Jain's fairness index. Fig.…”

Section: Performance Evaluation In 4g-5g Networkmentioning

confidence: 99%

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Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

Antonioli

Sousa

Rodrigues

et al. 2020

JCIS

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Supporting the dual connectivity technology in non-stand-alone 5G scenarios involves three main functionalities: a user device (UD)-base station (BS) association (UBA) mechanism, a flow control algorithm (FCA) to determine the data split transmitted by each BS and radio resource management (RRM) algorithms. However, previous works only focused on the design of at most two of these functionalities. In this letter, we go beyond and design a unified scheduling framework including the three aforementioned functionalities. The proposed framework targets the user satisfaction maximization and runs in a completely decentralized fashion, where the UBA algorithm runs independently at each UD, the FCA is executed at the master LTE BS and the RRM is performed individually by each BS. Simulations show the gains of the proposed solution compared to existing solution that addressed at most two functionalities.

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User Scheduling for Sum-Rate Maximization Under Minimum Rate Constraints for the MIMO IBC

Cited by 10 publications

References 10 publications

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

Joint Antenna and User Scheduling in the Massive MIMO System Over Time-Varying Fading Channels

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

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