1,594 research outputs found
Energy saving market for mobile operators
Ensuring seamless coverage accounts for the lion's share of the energy
consumed in a mobile network. Overlapping coverage of three to five mobile
network operators (MNOs) results in enormous amount of energy waste which is
avoidable. The traffic demands of the mobile networks vary significantly
throughout the day. As the offered load for all networks are not same at a
given time and the differences in energy consumption at different loads are
significant, multi-MNO capacity/coverage sharing can dramatically reduce energy
consumption of mobile networks and provide the MNOs a cost effective means to
cope with the exponential growth of traffic. In this paper, we propose an
energy saving market for a multi-MNO network scenario. As the competing MNOs
are not comfortable with information sharing, we propose a double auction
clearinghouse market mechanism where MNOs sell and buy capacity in order to
minimize energy consumption. In our setting, each MNO proposes its bids and
asks simultaneously for buying and selling multi-unit capacities respectively
to an independent auctioneer, i.e., clearinghouse and ends up either as a buyer
or as a seller in each round. We show that the mechanism allows the MNOs to
save significant percentage of energy cost throughout a wide range of network
load. Different than other energy saving features such as cell sleep or antenna
muting which can not be enabled at heavy traffic load, dynamic capacity sharing
allows MNOs to handle traffic bursts with energy saving opportunity.Comment: 6 pages, 2 figures, to be published in ICC 2015 workshop on Next
Generation Green IC
Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks
A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing
In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach
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