DAWN: Delay-Aware Wi-Fi Offloading and Network Selection

Cheung, Man Hon; Huang, Jianwei

doi:10.1109/jsac.2015.2416989

Cited by 74 publications

(40 citation statements)

References 20 publications

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“…Over the time horizon, the MUs move in the service region L following a Markov mobility model. Such a mobility model is widely used in the literature [45], [46]. Let N k b,i be the set of MUs appearing in the coverage of a BS b ∈ B at a scheduling slot k ∈ N + that are subscribed to SP i ∈ I, then N i = ∪ b∈B N k b,i , ∀k ∈ N + .…”

Section: A Inter-tenant Channel Auctionmentioning

confidence: 99%

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Chen

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

125

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With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

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Section: A Inter-tenant Channel Auctionmentioning

confidence: 99%

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Chen

Zhao

et al. 2019

IEEE J. Select. Areas Commun.

125

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“…Different approaches to implement WiFi offloading and to improve the performance of WiFi offloading were investigated in the works of Sankaran, Aijaz et al, and Dong et al In the work of Liu et al, the distributed data offloading algorithm for software‐defined networking based on an alternating direction method of multipliers is presented. The goal of the work of Cheung and Huang is to obtain a balance between the QoS and the users payment. In brief, the delay‐tolerant data offloading algorithm and general delay aware WiFi offloading are proposed to obtain the quality of the delivered service and the users payment.…”

Section: Introductionmentioning

confidence: 99%

Radio resource allocation in heterogeneous cellular networks based on effective capacity maximization: Perspective mobile data offloading

Gholami

Azmi

Mokari

et al. 2017

Trans Emerging Tel Tech

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In this paper, a new radio resource allocation approach for mobile data offloading, by means of a local access point (LAP) in the heterogeneous cellular networks is proposed. We consider the uplink path in a single‐carrier frequency‐division multiple access–based multiuser system in a 2‐tier cellular network with a macro base station (BS) and set of LAPs. We assume that there is no internetwork interference between macro BS and LAPs due to the use of 2 different sets of frequencies. Our aim is to allocate resources to determine the best strategy for providing the required quality of service with the criterion of minimum delay in terms of effective capacity maximization. The basis of this work is optimization for the strategy selection. In other words, the selection of the best station and power allocation for offloading in a single‐carrier frequency‐division multiple access multiuser system and solving the problems using the nonlinear optimization with the mesh adaptive direct algorithm solver and the dual decomposition method, respectively. The numerical results validate our analytical outcomes. For example, system delay decreases approximately 10 times if the traffic load of macro BS is decreased by 50%. In addition, we present a comparison of the impact of traffic reduction and consumed bandwidth due to offloading by means of LAPs on the cellular network, which confirms the efficiency of the proposed approach.

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“…On the other hand, papers [10] [11] [12] [13] have considered offloading delay-tolerant traffic from the MUs' perspective. In [10], Balasubramanian et al implemented a prototype system called Wiffler to leverage delay-tolerant traffic and fast switching to 3G.…”

mentioning

confidence: 99%

“…A MU's mobility pattern was predicted by a second-order Markov chain. In [13], Cheung studied the problem of offloading delay-tolerant applications for each user. A Markov decision process was formulated to minimize total data usage payment.…”

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Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process

Cheng

Liu

et al. 2018

IEICE Trans. Commun.

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Abstract-With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms.Index Terms-wireless LAN, multiple-flow, mobile data offloading, Markov decision processThe mobile data traffic demand is growing rapidly. According to the investigation of Cisco Systems [1], the mobile data traffic is expected to reach 24.3 exabytes per month by 2019, while it was only 2.5 exabytes per month at the end of 2014. On the other hand, the growth rate of the mobile network capacity is far from satisfying that kind of the demand, which has become a major problem for wireless mobile network operators (MNOs). Even though 5G technology is promising for providing huge wireless network capacity [2], the development process is long and the cost is high. Economic methods such as time-dependent pricing [3] [4] have been proposed to change users' usage pattern, which are not user-friendly. Up to now, the best practice for increasing mobile network capacity is to deploy complementary networks (such as wireless LAN and femtocells), which can be quickly deployed and are cost-efficient. Using such methods, part of the MUs' traffic demand can be offloaded from a MNO's cellular network to the complementary network.The process that a mobile device automatically changes its connection type (such as from cellular network to wireless LAN) is called vertical handover [5]. Mobile data offloading is facilitated by new standards such as Hotspot 2.0 [6] and the 3GPP Access Network Discovery and Selection Function (ANDSF) standard [7], with which information of network (such as price and network load) can be broadcasted to MUs in real-time. Then MUs can make offloading decisions intelligently based on the real-time network information.There are many works related to the wireless LAN offloading problem. However, p...

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DAWN: Delay-Aware Wi-Fi Offloading and Network Selection

Cited by 74 publications

References 20 publications

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach

Radio resource allocation in heterogeneous cellular networks based on effective capacity maximization: Perspective mobile data offloading

Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process

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