Joint Beamforming Coordination and User Selection for CoMP-Enabled NR-U Networks

Chen, Qimei; Yang, Kexin; Jiang, Hao; Qiu, Meikang

doi:10.1109/jiot.2021.3063733

Cited by 30 publications

(24 citation statements)

References 31 publications

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“…In the performance evaluation, a JT-CoMP is enabled at system-level simulation. The UE downlink model of the Vienna 5G simulation [35] is extended by the inter-and intrasite functionalities supported by a timer for synchronization that is implemented in Algorithm 1. The simulator runtime with the conventional and contributed models was observed, and results were achieved.…”

Section: Resultsmentioning

confidence: 99%

“…We observed that, higher fairness index in intersite means that, the system attempts to maintain the UE throughput similar for all the IoT nodes in the network regardless of their locations relative to eNB and received SINR [33][34][35]. According to the fairness index for intraand intersites, a higher number of users gives less fairness index in the two cases of intersite and intrasite CoMP, because the competition between users becomes tenser.…”

Section: Ue Wideband Sinrmentioning

confidence: 95%

See 1 more Smart Citation

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Mukhtar

Saeed

Mokhtar

et al. 2022

Wireless Communications and Mobile Computing

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Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.

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Section: Resultsmentioning

confidence: 99%

Section: Ue Wideband Sinrmentioning

confidence: 95%

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Mukhtar

Saeed

Mokhtar

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Note that the objective function of problem ( 13) is an upper boundary of the objective function of problem ( 9), while the feasible set of problem ( 13) is a subset of the feasible set of problem (9). Consequently, the solution obtained via problem ( 13) is an upper boundary of problem (9).…”

Section: Design Of Optimization Algorithmmentioning

confidence: 99%

“…Its optimization objective function was maximizing user rate with allowable transmission power constraint and a 0 − 1 diagonal digital precoding matrix, but without considering the demand of minimum rate of each user. The authors of [9] considered the joint coordinated BF and US for coordinated multi-point enabled new radio in unlicensed spectrum networks, with also aiming at maximizing the throughput instead of considering the rate demand for each user.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the Set Cardinality of Users Scheduled for Ultra-dense uRLLC Networks

Yuan

et al. 2021

Preprint

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Ultra-reliability and low latency communication plays an important role in the fifth and sixth generation communication systems. Among the different research issues, scheduling as many users as possible to serve on the limited time-frequency resource is a crucial topic, with requirement of the maximum allowable transmission power and the minimum rate requirement of each user. We address it by proposing a mixed integer programming model, with objective function is maximizing the set cardinality of users instead of maximizing the system sum rate. Mathematical transformations and successive convex approximation are combined to solve the problem. Numerical results show that the proposed method achieves a considerable performance compared with exhaustive search method, but with lower computational complexity.

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“…It is worth highlighting, in the FP techniques [20], [27], [28], all non-convex constraints are approximated with the sequence of convex subset, and the underlying convex subproblem is then iteratively solved until the convergence of objective function to a local solution. The FP quadratic transform based solutions have been widely used in many applications, e.g., power control, energy efficiency, and multi-antenna interference coordination [20], [27], [28]. For example, the SINR relaxation via FP techniques is provided for downlink in [20, Section IV], [28, Section III] and for uplink in [27, Section V], assuming perfect CSI and no blockages.…”

Section: Iterative Algorithms For Problem (16)mentioning

confidence: 99%

Latency-aware Reliable mmWave Communication via Multi-point Connectivity

Kumar

Joshi

Tölli

2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving lowlatency and reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We provide an iterative algorithm for the time-average sumpower-minimization problem by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform a time-average stochastic problem into a sequence of deterministic subproblems. Furthermore, for the robust beamformer design, we consider a pessimistic estimate of the userspecific rate, assuming that a portion of CoMP links would be blocked during the data transmission phase, while ensuring the average latency requirements. The numerical examples illustrate that in the presence of random blockages, the proposed method outperforms baseline scenarios and results in energy-efficient, high-reliability and low-latency mmWave communication.

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Joint Beamforming Coordination and User Selection for CoMP-Enabled NR-U Networks

Cited by 30 publications

References 31 publications

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load

Maximizing the Set Cardinality of Users Scheduled for Ultra-dense uRLLC Networks

Latency-aware Reliable mmWave Communication via Multi-point Connectivity

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