Resource Allocation for an OFDMA Cloud-RAN of Small Cells Underlaying a Macrocell

Abdelnasser, Amr; Hossain, Ekram

doi:10.1109/tmc.2015.2513052

Cited by 47 publications

(25 citation statements)

References 34 publications

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“…The UE admission control and total NPC minimization were jointly optimized in [20], where a single-stage optimization problem was formulated by introducing a weighting factor in the admission control part. Recently, [21] extended the work in [20] to multi-channel heterogeneous C-RAN where the C-RAN is overlaid by a macro-cell.…”

mentioning

confidence: 99%

Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN With Incomplete CSI

Pan

Zhu

Gomes

et al. 2017

IEEE J. Select. Areas Commun.

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This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

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confidence: 99%

Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN With Incomplete CSI

Pan

Zhu

Gomes

et al. 2017

IEEE J. Select. Areas Commun.

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“…where r k is obtained by substituting w into (9). Then, {U , w } satisfies Constraint C5 of Problem P 7 .…”

Section: Lemmamentioning

confidence: 99%

“…On the other hand, if there exists at least one UE whose data rate is strictly larger than its rate requirement, i.e., r # k > R k,min . Then, we can adopt the iterative scaling algorithm given in Appendix A of [23] to construct another set of beamforming vectors w ## such that r ## k = R k,min , ∀k, where r ## k is obtained by substituting w ## into (9). As a result, U # , w ## is feasible for Problem P 1 .…”

Section: Lemmamentioning

confidence: 99%

Robust Beamforming Design for Ultra-Dense User-Centric C-RAN in the Face of Realistic Pilot Contamination and Limited Feedback

Pan

Ren

Elkashlan

et al. 2019

IEEE Trans. Wireless Commun.

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The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads (RRHs) are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex (FDD) UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim for optimizing the beamforming vectors in order to minimize the total transmit power subject to users' rate requirements and fronthaul capacity constraints. We derive the closedform expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms are provided to guarantee the feasibility of the problem. Simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA is capable of achieving good performance. Fronthaul Links BBU Pool BBU BBU BBU UE 1 RRH 1 RRH 2 UE 2 UE 3 UE 4 UE 5 UE 6 RRH 3 RRH 4 RRH 5 RRH 6 RRH 7 RRH 8 RRH 9 BBU Fig. 1. Illustration of a UD-CRAN with nine RRHs and six UEs, i.e., I = 9, K = 6. To reduce the complexity, each UE is served by the RRHs within the dashed circle centered around the UE. achieving a high system throughput [1]. In UDNs, the average distance between small BSs and users can be dramatically reduced, which can translate into improved link reliability. However, since all small BSs reuse the same frequency, the users are also exposed to severe inter-cell interference, which is a severe performance limiting factor. Hence, the interference should be judiciously managed in order to reap the potential benefits of UDNs. As a result, the cloud radio access network (CRAN) concept has been recently proposed as a promising network architecture [2]. In CRAN, all the signal processing tasks are performed at the BBU pool, and all the conventional small BSs are replaced by low-cost low-power RRHs, which are only responsible for simple transmission/reception functions. The RRHs are connected to the BBU pool through the fronthaul links to support the centralized signal processing. Hence, the interference in the network can be effectively mitigated by employing the coordinated multipoint (CoMP) technique.Furthermore, due to their low-complexity functionalities, the mobile operator can densely deploy the RRHs at a low capital cost. Hence, the CRAN architecture is an ideal platform for supporting UDNs. Th...

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“…Moreover, in terms of radio resource management (RRM), we simply allocate the available bandwidth evenly to UEs. Optimal allocation of radio resource is worth investigating to achieve a larger network capacity [28], [29].…”

Section: Limitations and Concluding Remarksmentioning

confidence: 99%

Optimized deployment of drone base station to improve user experience in cellular networks

Huang

Savkin

Ding

et al. 2019

Journal of Network and Computer Applications

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Resource Allocation for an OFDMA Cloud-RAN of Small Cells Underlaying a Macrocell

Cited by 47 publications

References 34 publications

Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN With Incomplete CSI

Joint User Selection and Energy Minimization for Ultra-Dense Multi-channel C-RAN With Incomplete CSI

Robust Beamforming Design for Ultra-Dense User-Centric C-RAN in the Face of Realistic Pilot Contamination and Limited Feedback

Optimized deployment of drone base station to improve user experience in cellular networks

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