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A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs,” http://arxiv.org/abs/1311.5114, 2013. BIOGRAPHIES FEDERICO BOCCARDI (federico.boccardi@vodafone.com) is a principal engineer in Vodafone. He recei
- Padova, Italy, in 2002 and 2007 respectively
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Front Cover: Illustration of Base Station Coordination in a Two-Cell Network
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Clustering with Multiple Receiving Antennas in Downlink FDD CoMP Systems
"... Abstract-Inter-cell interference in downlink cellular networks can be managed by coordination among the base stations (BSs). Constraints on the backhaul throughput make full coordination still challenging and typically clusters of BSs are organized to serve the user equipments (UEs). Joint precodin ..."
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Abstract-Inter-cell interference in downlink cellular networks can be managed by coordination among the base stations (BSs). Constraints on the backhaul throughput make full coordination still challenging and typically clusters of BSs are organized to serve the user equipments (UEs). Joint precoding within each cluster is designed to deal with intra-cluster interference. Moreover, inter-cluster interference (ICI) can be reduced by implementing dynamic clustering, i.e., by changing BS clusters over time to provide more fairness among the UEs. In this work we assume that UEs are equipped with multiple antennas and use an interference rejection combiner to suppress the ICI not managed by precoders at transmitter side. In this framework, we develop an algorithm that dynamically organizes clusters and schedules UEs in each cluster by requiring a channel state information at the transmitter which is independent of the number of receiving antennas. Simulations provide two main results: a) a considerable improvement is achieved by adding antennas at the UE and b) the gain of dynamic clustering over static clustering sensibly decreases by equipping the UEs with more antennas.
Spécialité ”Communication et Électronique“
, 2012
"... pour obtenir le grade de docteur délivré par ..."
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"... Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing a ..."
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Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we exploit the near-sparsity of large C-RAN channel matrices, and derive a unified theoretical framework for clustering and parallel processing. Based on the framework, we propose a dynamic nested clustering (DNC) algorithm for uplink signal detection. This algorithm allows flexible trade-offs between system performance and other critical parameters, such as computational complexity and channel estimation overhead. Moreover, the algorithm is amenable to parallel processing, and various parallel implementations are discussed for different types of data center architectures. With the proposed algorithm, we show that the computation time for the optimal linear detector can be reduced from O(N3) to no higher than O(N 42 23), where N is the number of RRHs in C-RAN.
Coordinated Multipoint (CoMP) Transmission Design for Cloud-RANs with Limited Fronthaul Capacity Constraints
"... IEEE Abstract—In this paper, we consider the CoMP transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs) serving each user as well as the precoding and transmission powers to minimize the total transmission power ..."
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IEEE Abstract—In this paper, we consider the CoMP transmission design for the downlink cloud radio access network (Cloud-RAN). Our design aims to optimize the set of remote radio heads (RRHs) serving each user as well as the precoding and transmission powers to minimize the total transmission power while maintaining the fronthaul capacity and users ’ QoS con-straints. The fronthaul capacity constraint involves a non-convex and discontinuous function which renders the optimal exhaustive search method unaffordable for large networks. To address this challenge, we propose two low-complexity algorithms. The first pricing-based algorithm solves the underlying problem through iteratively tackling a related pricing problem while appropriately updating the pricing parameter. In the second iterative linear-relaxed algorithm, we directly address the fronthaul constraint function by iteratively approximating it with a suitable linear form using a conjugate function and solving the corresponding convex problem. For performance evaluation, we also compare our proposed algorithms with two existing algorithms in the literature. Finally, extensive numerical results are presented which illustrate the convergences of our proposed algorithms and confirm that our algorithms significantly outperform the state-of-the-art existing algorithms. Index Terms—Cloud radio access network (Cloud-RAN), pre-coding, power minimization, resource allocation, limited fron-thaul capacity. I.
Adaptive Block Diagonalization and User Scheduling With Out of Cluster Interference
"... c © c © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistributio ..."
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c © c © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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"... Abstract—In this work, we consider cloud RAN architecture and focus on the downlink of an antenna domain (AD) exposed to external interference from neighboring ADs. The propagation scenario considered is of dense outdoor with Rician, line-of-sight (correlated) channels. With system sum-rate as perfo ..."
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Abstract—In this work, we consider cloud RAN architecture and focus on the downlink of an antenna domain (AD) exposed to external interference from neighboring ADs. The propagation scenario considered is of dense outdoor with Rician, line-of-sight (correlated) channels. With system sum-rate as performance metric, and assuming that perfect channel state information is available at the aggregation node (AN), we implement i) a greedy user association algorithm, and ii) a greedy remote radio-head (RRH) clustering algorithm at the AN. We then evaluate and compare the sum-rate gains due to two distinct transmit precoding schemes namely i) zero forcing beamforming (ZFBF), ii) coordinated beamforming (CB), when exposed to external interference of same kind. We also discuss in detail the cost of RRH clustering, i.e., the piloting overhead (and the elements driving it), and incorporate its impact on system sum-rate. From system-level simulation results, we learn that in an interference-limited regime, i) RRH clustering helps, i.e., cost-adjusted performance when RRHs cooperate is superior to the performance when they don’t, ii) for transmit precoding, the CB scheme is to be preferred over the ZFBF scheme. I.
Scalable Coordinated Uplink Processing in Cloud Radio Access Networks
"... Abstract—Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal pro ..."
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Abstract—Featured by centralized processing and cloud based infrastructure, Cloud Radio Access Network (C-RAN) is a promising solution to achieve an unprecedented system capacity in future wireless cellular networks. The huge capacity gain mainly comes from the centralized and coordinated signal processing at the cloud server. However, full-scale coordination in a large-scale C-RAN requires the processing of very large channel matrices, leading to high computational complexity and channel estimation overhead. To resolve this challenge, we show in this paper that the channel matrices can be greatly sparsified without substantially compromising the system capacity. Through rigorous analysis, we derive a simple threshold-based channel matrix sparsification approach. Based on this approach, for reasonably large networks, the non-zero entries in the channel matrix can be reduced to a very low percentage (say 0.13 % ∼ 2%) by compromising only 5 % of SINR. This means each RRH only needs to obtain the CSI of a small number of closest users, resulting in a significant reduction in the channel estimation overhead. On the other hand, the high sparsity of the channel matrix allows us to design detection algorithms that are scalable in the sense that the average computational complexity per user does not grow with the network size. I.
Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing
"... Abstract—A novel dynamic radio-cooperation strategy is pro-posed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capa-bilities of C-RANs in computing-resource sharing and real-tim ..."
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Abstract—A novel dynamic radio-cooperation strategy is pro-posed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capa-bilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conven-tional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources. Index Terms—Cloud radio access networks; dynamic cluster-ing; joint beamforming; computing resource sharing. I.
