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Register a new userPolicy Optimization for Content Push via Energy Harvesting Small Cells in Heterogeneous Networks
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Added by
Jie Gong
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS (MBS) instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS. With finite battery capacity, Markov decision process based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based (POTB) policy and the energy-efficient threshold-based (EETB) policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based (GOTB) policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high.
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Motivated by the recent development of energy harvesting communications, and the trend of multimedia contents caching and push at the access edge and user terminals, this paper considers how to design an effective push mechanism of energy harvesting powered small-cell base stations (SBSs) in heterogeneous networks. The problem is formulated as a Markov decision process by optimizing the push policy based on the battery energy, user request and content popularity state to maximize the service capability of SBSs. We extensively analyze the problem and propose an effective policy iteration algorithm to find the optimal policy. According to the numerical results, we find that the optimal policy reveals a state dependent threshold based structure. Besides, more than 50% performance gain is achieved by the optimal push policy compared with the non-push policy.
The explosive growth of mobile multimedia traffic calls for scalable wireless access with high quality of service and low energy cost. Motivated by the emerging energy harvesting communications, and the trend of caching multimedia contents at the access edge and user terminals, we propose a paradigm-shift framework, namely GreenDelivery, enabling efficient content delivery with energy harvesting based small cells. To resolve the two-dimensional randomness of energy harvesting and content request arrivals, proactive caching and push are jointly optimized, with respect to the content popularity distribution and battery states. We thus develop a novel way of understanding the interplay between content and energy over time and space. Case studies are provided to show the substantial reduction of macro BS activities, and thus the related energy consumption from the power grid is reduced. Research issues of the proposed GreenDelivery framework are also discussed.
Designing decentralized policies for wireless communication networks is a crucial problem, which has only been partially solved in the literature so far. In this paper, we propose the Decentralized Markov Decision Process (Dec-MDP) framework to analyze a wireless sensor network with multiple users which access a common wireless channel. We consider devices with energy harvesting capabilities, so that they aim at balancing the energy arrivals with the data departures and with the probability of colliding with other nodes. Randomly over time, an access point triggers a SYNC slot, wherein it recomputes the optimal transmission parameters of the whole network, and distributes this information. Every node receives its own policy, which specifies how it should access the channel in the future, and, thereafter, proceeds in a fully decentralized fashion, without interacting with other entities in the network. We propose a multi-layer Markov model, where an external MDP manages the jumps between SYNC slots, and an internal Dec-MDP computes the optimal policy in the near future. We numerically show that, because of the harvesting, a fully orthogonal scheme (e.g., TDMA-like) is suboptimal in energy harvesting scenarios, and the optimal trade-off lies between an orthogonal and a random access system.
Energy harvesting multi-hop networks allow for perpetual operation of low cost, limited range wireless devices. Compared with their battery operated counterparts, the coupling of energy and data causality constraints with half duplex relay operation makes it challenging to operate such networks. In this paper, a throughput maximization problem for energy harvesting two-hop networks with decode-and-forward half-duplex relays is investigated. For a system with two parallel relays, various combinations of the following four transmission modes are considered: Broadcast from the source, multi-access from the relays, and successive relaying phases I and II. Optimal transmission policies for one and two parallel relays are studied under the assumption of non-causal knowledge of energy arrivals and finite size relay data buffers. The problem is formulated using a convex optimization framework, which allows for efficient numerical solutions and helps identify important properties of optimal policies. Numerical results are presented to provide throughput comparisons and to investigate the impact of multiple relays, size of relay data buffers, transmission modes, and energy harvesting on the throughput.
In this paper, we investigate different secrecy energy efficiency (SEE) optimization problems in a multiple-input single-output underlay cognitive radio (CR) network in the presence of an energy harvesting receiver. In particular, these energy efficient designs are developed with different assumptions of channels state information (CSI) at the transmitter, namely perfect CSI, statistical CSI and imperfect CSI with bounded channel uncertainties. In particular, the overarching objective here is to design a beamforming technique maximizing the SEE while satisfying all relevant constraints linked to interference and harvested energy between transmitters and receivers. We show that the original problems are non-convex and their solutions are intractable. By using a number of techniques, such as non-linear fractional programming and difference of concave (DC) functions, we reformulate the original problems so as to render them tractable. We then combine these techniques with the Dinkelbachs algorithm to derive iterative algorithms to determine relevant beamforming vectors which lead to the SEE maximization. In doing this, we investigate the robust design with ellipsoidal bounded channel uncertainties, by mapping the original problem into a sequence of semidefinite programs by employing the semidefinite relaxation, non-linear fractional programming and S-procedure. Furthermore, we show that the maximum SEE can be achieved through a search algorithm in the single dimensional space. Numerical results, when compared with those obtained with existing techniques in the literature, show the effectiveness of the proposed designs for SEE maximization.
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