Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

Dai, Binbin; Yu, Wei

doi:10.1109/access.2014.2362860

Cited by 366 publications

(15 citation statements)

References 46 publications

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“…However, the quantization process, necessitated by the limited capacity of the fronthaul links, generates quantization noise diminishing the system performance (Park et al, 2013). As opposed to this, using data-sharing, the CP performs channel encoding and the BSs then continue with the remaining baseband processing tasks (Wei Yu and Yu, 2014). Therefore, under the data-sharing strategy, the BSs have more computational resources and responsibilities.…”

Section: Overviewmentioning

confidence: 99%

Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks

et al. 2021

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Rate-splitting multiple access (RSMA) has been recognized as a promising physical layer strategy for 6G. Motivated by the ever-increasing popularity of cache-enabled content delivery in wireless communications, this paper proposes an innovative multigroup multicast transmission scheme based on RSMA for cache-aided cloud-radio access networks (C-RAN). Our proposed scheme not only exploits the properties of content-centric communications and local caching at the base stations (BSs) but also incorporates RSMA to better manage interference in multigroup multicast transmission with statistical channel state information (CSI) known at the central processor (CP) and the BSs. At the RSMA-enabled cloud CP, the message of each multicast group is split into a private and a common part with the former private part being decoded by all users in the respective group and the latter common part being decoded by multiple users from other multicast groups. Common message decoding is done for the purpose of mitigating the interference. In this work, we jointly optimize the clustering of BSs and the precoding with the aim of maximizing the minimum rate among all multicast groups to guarantee fairness serving all groups. The problem is a mixed-integer nonlinear stochastic program (MINLSP), which is solved by a practical algorithm we propose including a heuristic clustering algorithm for assigning a set of BSs to serve each user followed by an efficient iterative algorithm that combines the sample average approximation (SAA) and weighted minimum mean square error (WMMSE) to solve the stochastic non-convex subproblem of precoder design. Numerical results show the explicit max-min rate gain of our proposed transmission scheme compared to the state-of-the-art trivial interference processing methods. Therefore, we conclude that RSMA is a promising technique for cache-aided C-RAN.

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Section: Overviewmentioning

confidence: 99%

Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks

et al. 2021

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“…The adopted channel model is standardized by the 3rd Generation Partnership Project (3GPP) (3GPP, 2015) and used in most of the works in the literature, e.g., Björnson and Jorswieck, 2013;Shi et al, 2014;Wei Yu and Yu, 2014: h n,k D n,k e n,k .…”

Section: Simulation Parameters and Studied Schemesmentioning

confidence: 99%

Power Minimization Using Rate Splitting With Statistical CSI in Cloud-Radio Access Networks

et al. 2021

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Minimizing the power consumption in mobile communication networks while ensuring a minimum quality of service (QoS) for applications is essential in light of the unprecedented expected increase in the number of connected devices and the associated data traffic beyond the fifth generation of wireless networks (B5G). This paper considers a cloud-radio access network (C-RAN) model where a central processor (CP) is connected to the base stations (BSs) via limited capacity fronthaul links. In the context of our C-RAN setting, we consider the practical case where the CP has only statistical knowledge of channel state information (CSI). While conventional wireless systems adopt the treating interference as noise (TIN) strategy to deal with the interference in the network, this paper instead considers that the CP applies the rate splitting (RS) strategy by dividing each user’s message into two parts: a private part to be decoded by the intended user only and a common part to be decoded by a subset of users, for the sole reason of interference mitigation in the network. To best account for the channel estimation errors, this paper addresses the problem of transmit power minimization under minimum QoS constraints on the achievable ergodic rate per user, so as to determine the beamforming vectors of the private and common messages as well as the rate allocated to all the users. The considered problem is of stochastic, complex, and non-convex nature. This paper addresses the problem intricacies through an iterative approach that leverages both the sample average approximation (SAA) technique and the weighted minimum mean squared error (WMMSE) algorithm to obtain a stationary point of the optimization problem in the asymptotic regime. The numerical results demonstrate the gain achieved with the RS strategy as compared to TIN, especially under high QoS requirements.

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“…We focus on weighted sum-rate maximization subject to fronthaul constraints as in [5]. Optimal user clustering and beamforming vectors are determined at the BBU pool using global CSI.…”

Section: Reference Centralized Algorithm For Cranmentioning

confidence: 99%

“…where α k are weight parameters to achieve different fairness levels among users. The first constraint is given by the maximum power for each AP r, the second one is the per-AP fronthaul rate constraint, and the third one expresses the achievable rate for each user k. This is a non-convex optimization problem for which a weighted MMSE-based algorithm was proposed [5], [6]. The case with perfect CSI represents the ideal scenario in terms of system performance, but requires full CSI feedback for all users from each AP, resulting into a significant burden over bandwidth-limited fronthaul links.…”

Section: Reference Centralized Algorithm For Cranmentioning

confidence: 99%

“…In addition, CRAN enables optimal joint baseband signal processing, radio resource allocation and interference management but at the expense of heavily burdening the capacity-limited fronthaul links [3], [4]. Hence, many CRAN related research works have focused on the optimization of user clustering and beamforming under fronthaul capacity constraints [5]- [10]. Another major drawback of the CRAN centralized architecture is the additional network latency introduced by the fronthaul links, making it unsuited for the highly delay-sensitive applications envisioned in 5G.…”

Section: Introductionmentioning

confidence: 99%

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User Pre-Scheduling and Beamforming with Imperfect CSI for Future Cloud/Fog-Radio Access Networks

Kaneko

Boukhatem

Pontois

et al. 2019

IEICE Trans. Commun.

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By incorporating cloud computing capabilities to provide radio access functionalities, Cloud Radio Access Networks (CRANs) are considered to be a key enabling technology of future 5G and beyond communication systems. In CRANs, centralized radio resource allocation optimization is performed over a large number of small cells served by simple access points, the Remote Radio Heads (RRHs). However, the fronthaul links connecting each RRH to the cloud introduce delays and entail imperfect Channel State Information (CSI) knowledge at the cloud processors. In order to satisfy the stringent latency requirements envisioned for 5G applications, the concept of Fog Radio Access Networks (FogRANs) has recently emerged for providing cloud computing at the edge of the network. Although FogRAN may alleviate the latency and CSI quality issues of CRAN, its distributed nature degrades network interference mitigation and global system performance. Therefore, we investigate the design of tailored user pre-scheduling and beamforming for FogRANs. In particular, we propose a hybrid algorithm that exploits both the centralized feature of the cloud for globally-optimized pre-scheduling using imperfect global CSIs, and the distributed nature of FogRAN for accurate beamforming with high quality local CSIs. The centralized phase enables the interference patterns over the global network to be considered, while the distributed phase allows for latency reduction, in line with the requirements of FogRAN applications. Simulation results show that our proposed algorithm outperforms the baseline algorithm under imperfect CSIs, jointly in terms of throughput, energy efficiency, as well as delay.

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Sparse Beamforming and User-Centric Clustering for Downlink Cloud Radio Access Network

Cited by 366 publications

References 46 publications

Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks

Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks

Power Minimization Using Rate Splitting With Statistical CSI in Cloud-Radio Access Networks

User Pre-Scheduling and Beamforming with Imperfect CSI for Future Cloud/Fog-Radio Access Networks

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