Joint cell selection and resource allocation games with backhaul constraints

Ortín, Jorge; Gállego, José Ramón; Canales, María

doi:10.1016/j.pmcj.2016.06.009

Cited by 12 publications

(24 citation statements)

References 17 publications

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“…The authors of [12] investigated resource allocation of CoMP transmission in C-RANs, and proposed a fairness-based scheme for enhancing the network coverage. In [13], the authors studied the joint cell-selection and resource allocation problem, in C-RANs without CoMP. In [14], a resource allocation problem was studied for C-RANs with a framework of small cells underlaying a macro cell.…”

Section: B Related Work 1) Rrh Selection and Resource Allocationmentioning

confidence: 99%

“…Solving an iterated function lies on finding a fixed point of it. The problem is formulated in (13) below, in which R j (j ∈ J) and J i (i ∈ R) are given.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

“…Next we derive a solution method that achieves the global optimum of (13). We define function F α as follows.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

“…Theorem 5 gives the solution method for solving (20) (and equivalently (13)). The optimality of the method is guaranteed by Theorem 6.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

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User-Centric Performance Optimization With Remote Radio Head Cooperation in C-RAN

You

Yuan

2020

IEEE Trans. Wireless Commun.

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In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs' capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., resource allocation (and CoMP selection). We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at maximumly scaling up the capacity for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.

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Section: B Related Work 1) Rrh Selection and Resource Allocationmentioning

confidence: 99%

“…Solving an iterated function lies on finding a fixed point of it. The problem is formulated in (13) below, in which R j (j ∈ J) and J i (i ∈ R) are given.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

“…Next we derive a solution method that achieves the global optimum of (13). We define function F α as follows.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

“…Theorem 5 gives the solution method for solving (20) (and equivalently (13)). The optimality of the method is guaranteed by Theorem 6.…”

Section: B Computing the Optimum For Given Comp Selectionmentioning

confidence: 99%

See 2 more Smart Citations

User-Centric Performance Optimization With Remote Radio Head Cooperation in C-RAN

You

Yuan

2020

IEEE Trans. Wireless Commun.

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“…However, because of the NP-hard and combinatorial features of joint CARA problem, it is challenging to achieve a globally optimal solution. There have been some approaches to solve the CARA problem, such as graph theory approach [16], integer programming method [17], matching game solution [18], and stochastic geometric strategy [19]. These approaches still were limited to solve the joint CARA problem as they needed nearly precise information such as full knowledge of channel state information (CSI) or fading models.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles

Kwon

Kim²

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Millimeter-wave (mmWave) base station can offer abundant high capacity channel resources toward connected vehicles so that quality-of-service (QoS) of them in terms of downlink throughput can be highly improved. The mmWave base station can operate among existing base stations (e.g., macro-cell base station) on non-overlapped channels among them and the vehicles can make decision what base station to associate, and what channel to utilize on heterogeneous networks. Furthermore, because of the non-omni property of mmWave communication, the vehicles decide how to align the beam direction toward mmWave base station to associate with it. However, such joint problem requires high computational cost, which is NP-hard and has combinatorial features. In this paper, we solve the problem in 3-tier heterogeneous vehicular network (HetVNet) with multiagent deep reinforcement learning (DRL) in a way that maximizes expected total reward (i.e., downlink throughput) of vehicles. The multi-agent deep deterministic policy gradient (MADDPG) approach is introduced to achieve optimal policy in continuous action domain.

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