Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

Samarakoon, Sumudu; Bennis, Mehdi; Saad, Walid

doi:10.1109/twc.2013.092413.130221

Cited by 58 publications

(56 citation statements)

References 34 publications

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“…A pioneering work on backhaul-aware resource management is offered in [21] where authors propose a novel distributed reinforcement learning mechanism which benefits users served by macro-cells to improve their performance by selecting suitable small cells for data coordination. The small cells connect back to the macro-cell using either in-band wireless or wired backhaul links with limited capacity.…”

Section: ) Backhaul-delay-awarementioning

confidence: 99%

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

et al. 2016

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5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, selfoptimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes.INDEX TERMS Backhaul, fronthaul, user-centric, user-cell association, SON, reinforcement learning, multiple attribute decision making.

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Section: ) Backhaul-delay-awarementioning

confidence: 99%

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

et al. 2016

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“…Finally, by substituting (9), (11), and (15) into (8), we can estimate the downlink delay of each serving BS, which is required in our developed optimization problem (see above constraint (7e)).…”

Section: A Delaymentioning

confidence: 99%

“…Recently, a backhaul-aware interference management algorithm has been proposed [11], in which the condition of both radio access and heterogeneous backhaul (wireless and wired) with finite capacity is employed to optimize users' delay-rate trade-off. Generally, these efforts are valuable in the sense that they drew the attention of researchers to contemplate the issues of backhaul in greater depth.…”

Section: Introductionmentioning

confidence: 99%

Backhaul-Aware User Association in FiWi Enhanced LTE-A Heterogeneous Networks

Beyranvand

Lim

Maier

et al. 2015

IEEE Trans. Wireless Commun.

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In this paper, we shed some light on the latency and reliability issues of mobile backhaul networks, which have been largely ignored in the past, and examine their impact on LTE-A heterogeneous networks (HetNets). Specifically, we propose a backhaul-aware user association algorithm for fiberwireless (FiWi) enhanced LTE-A HetNets. The performance limiting factors of state-of-the-art fiber backhaul infrastructures are highlighted and a variety of solutions are described. In order to mitigate the vulnerability of the backhaul against fiber cuts, we introduce different advanced protection techniques. Accounting for the given conditions of the backhaul in terms of delay and reliability, we present a distributed load-balancing algorithm for user association in FiWi-LTE HetNets. The proposed algorithm is analyzed and evaluated numerically by comparing its performance with state-of-the-art alternative approaches in terms of average delay, blocking probability, average achievable throughput, and service interruption percentage. The obtained results demonstrate that our algorithm outperform its counterparts in terms of delay and service interruption percentage, while its average achievable throughput is the same as that of a backhaul-unaware alternative solution. In addition, the blocking probability of the proposed backhaul-aware load-balancing method is shown to be higher than that of backhaul-unaware ones.

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“…Authors in [6] analyze K-tier downlink heterogeneous cellular network and derive closed form expressions for coverage probability and average rate. In [7], the authors perform load-aware downlink modeling and the authors in [8] deal with interference management. Other analysis for load distribution in heterogeneous cellular networks is given in [9].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Two-Tier LTE Network with Randomized Resource Allocation and Proactive Offloading

Ichkov¹,

Atanasovski²,

Gavrilovska³

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract-The heterogeneity in cellular networks that comprise multiple base stations' types imposes new challenges in network planning and deployment. The Radio Resource Management (RRM) techniques, such as dynamic sharing of the available resources and advanced user association strategies determine the overall network capacity and the network/spectrum efficiency. This paper evaluates the downlink performance of a two-tier heterogeneous LTE network (consisting of macro and femto tiers) in terms of rate distribution, i.e. the percentage of users that achieve certain rate in the system. The paper specifically addresses: (1) the femto tier RRM by randomization of the allocated resources; (2) the user association process by introducing novel proactive offloading scheme and (3) femto tier access control. System level simulation results show that an optimal RRM strategy can be designed for different scenarios (e.g. congested and uncongested networks). The proposed proactive offloading scheme in the association phase improves the performance of congested networks by efficiently utilizing the available femto tier resources. Finally, the introduced hybrid access in femto tier is shown to perform nearly identical as the open access.

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Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

Cited by 58 publications

References 34 publications

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

Backhaul-Aware User Association in FiWi Enhanced LTE-A Heterogeneous Networks

Analysis of Two-Tier LTE Network with Randomized Resource Allocation and Proactive Offloading

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