Dynamic base station clustering and beamforming for an uplink SIMO cloud radio access network

Lin, Jie; Li, Qiang; Li, Yubai; Jiang, Changxu

doi:10.1109/iccps.2014.7062311

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…From (8) we can tell that the delay of wireless communication is independent of the caching process, while the processing delay is affected by the caching strategy of the BS cluster, which means that the service caching strategy depends on the clustering. Consequently, we solve the service caching problem based on the optimal BS clustering [18]. Naturally, our optimization goal is to minimize the averaged processing delay…”

Section: Service Offloading Modelmentioning

confidence: 99%

User-Centric Cooperative MEC Service Offloading

Chen

2021

Preprint

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Mobile edge computing provides users with a cloud environment close to the edge of the wireless network, supporting the computing intensive applications that have low latency requirements. The combination of offloading with the wireless communication brings new challenges. This paper investigates the service caching problem during the long-term service offloading in the user-centric wireless network. To meet the time-varying service demands of a typical user, a cooperative service caching strategy in the unit of the base station (BS) cluster is proposed. We formulate the caching problem as a time-averaged completion delay minimization problem and transform it into time-decoupled instantaneous problems with a virtual caching cost queue at first. Then we propose a distributed algorithm which is based on the consensus-sharing alternating direction method of multipliers to solve each instantaneous problem. The simulations validate that the proposed online distributed service caching algorithm can achieve the optimal time-averaged completion delay of offloading tasks with the smallest caching cost in the unit of a BS cluster.

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Section: Service Offloading Modelmentioning

confidence: 99%

User-Centric Cooperative MEC Service Offloading

Chen

2021

Preprint

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“…As a key technology in 5G and beyond [24], user-centric network (UCN) [25]- [27] breaks the concept of "cell" in traditional cellular-based network, which can be seen as a reliable solution to the above problems. In UCN, each user will be served by a dynamically divided BS set, which is called BS cluster [28], [29]. Each BS cluster can be divided adaptively according to the location and network condition of users to provide seamless wireless transmission service.…”

Section: Introductionmentioning

confidence: 99%

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by a dedicated access point (AP) cluster enabled with MEC capability instead of a single MEC server, however, at the expense of more energy consumption and greater privacy risks. To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchainenabled UC-MEC system where blockchain operations and resource optimization are jointly performed. Firstly, we design a resource-aware, reliable, replicated, redundant, and faulttolerant (R-RAFT) consensus mechanism to implement secure and reliable resource trading. Then, an optimization framework based on alternating direction method of multipliers (ADMM) is proposed to minimize the total energy consumed by wireless transmission, consensus and task computing, where APs clustering, computing resource allocation and bandwidth allocation are jointly considered. Simulation results show superiority of the proposed UC-MEC system over reference schemes, at most 33.96% reduction in the total delay and 48.77% reduction in the total energy consumption.

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“…The complex data model is concerned in [11], where the linear constraint is replaced by the unit-norm constraint. In this work, SDR is further used to handle this non-convex constraint.…”

Section: Introductionmentioning

confidence: 99%

Joint base station selection and beamforming in uplink SIMO networks with per-user power constraints

Jiang

Lin

Shao

2015

2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)

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Consider an uplink SIMO network consisting of multiple base stations (BSs) and multiple users, where each user has an individual transmit power constraint, and the BSs are allowed to cooperate in data receiving. To guarantee fairness among users and avoid heavy burden of backhaul data exchange, we maximize the minimum S-INR based on joint BS selection and beamforming. We formulate this problem from the perspective of sparse beamforming. However, the scaling ambiguity of the receive beamformers will make the sparse constraints trivial. Inspired by the observation that the transmit beamformer is immune to the scaling ambiguity, we apply the duality theorem to the uplink max-min problem under per-user power constraints, and reformulate it as an equivalent downlink problem. An iterative two-stage algorithm is then developed to solve the sparse beamforming problem efficiently. The effectiveness of the proposed algorithm is demonstrated by numerical simulations.Index Terms-Max-min fairness, base station selection, beamforming, uplink-downlink duality, per-user power constraint

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Dynamic base station clustering and beamforming for an uplink SIMO cloud radio access network

Cited by 6 publications

References 12 publications

User-Centric Cooperative MEC Service Offloading

User-Centric Cooperative MEC Service Offloading

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Joint base station selection and beamforming in uplink SIMO networks with per-user power constraints

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