Cellular networks are among the major energy hoggers of communication networks, and their contributions to the global energy consumption increase rapidly due to the surges of data traffic. With the development of green energy technologies, base stations (BSs) can be powered by green energy in order to reduce the on-grid energy consumption, and subsequently reduce the carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) that is determined by the size of the green energy generator, the battery capacity, and other installation expenses. In this paper, we introduce and investigate the green energy provisioning (GEP) problem which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the QoS requirements of cellular networks. The GEP problem is challenging because it involves the optimization over multiple time slots and across multiple BSs. We decompose the GEP problem into the weighted energy minimization problem and the green energy system sizing problem, and propose a green energy provisioning solution consisting of the provision cost aware traffic load balancing algorithm and the binary energy system sizing algorithm to solve the sub-problems and subsequently solve the GEP problem. We validate the performance and the viability of the proposed green energy provisioning solution through extensive simulations, which also conform to our analytically results.
To optimize the utilization of renewable energy, Ozel @cite_4 proposed to optimize the packet transmission policy for energy harvest wireless nodes. Zhou @cite_18 proposed the hand over parameter tuning algorithm and the power control algorithm to guide mobile users to access green energy powered BSs. Han and Ansari @cite_11 proposed an energy aware cell size adaptation algorithm named ICE, which balances the energy consumption among BSs powered by green energy, and enables more users to be served with green energy. Considering a network with multiple energy supplies, Han and Ansari @cite_7 also proposed to optimize the utilization of green energy, and reduce the on-grid energy consumption of cellular networks by the cell size optimization. Assuming the capacity of the green energy system is given, all these solutions are optimizing wireless cellular networks according to the availability of the green energy. However, for the GEP problem, the capacity of green energy system is to be determined. Therefore, the existing solutions on optimizing the green energy utilization cannot be directly applied to solve the GEP problem.
@cite_18 @cite_4 @cite_7 @cite_11
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This paper studies the resource allocation algorithm design for secure information and renewable green energy transfer to mobile receivers in distributed antenna communication systems. In particular, distributed remote radio heads (RRHs antennas) are connected to a central processor (CP) via capacity-limited backhaul links to facilitate joint transmission. The RRHs and the CP are equipped with renewable energy harvesters and share their energies via a lossy micropower grid for improving the efficiency in conveying information and green energy to mobile receivers via radio frequency signals. The considered resource allocation algorithm design is formulated as a mixed nonconvex and combinatorial optimization problem taking into account the limited backhaul capacity and the quality-of-service requirements for simultaneous wireless information and power transfer (SWIPT). We aim at minimizing the total network transmit power when only imperfect channel state information of the wireless energy harvesting receivers, which have to be powered by the wireless network, is available at the CP. In light of the intractability of the problem, we reformulate it as an optimization problem with binary selection, which facilitates the design of an iterative resource allocation algorithm to solve the problem optimally using the generalized Bender's decomposition (GBD). Furthermore, a suboptimal algorithm is proposed to strike a balance between computational complexity and system performance. Simulation results illustrate that the proposed GBD-based algorithm obtains the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. In addition, the distributed antenna network for SWIPT with renewable energy sharing is shown to require a lower transmit power compared with a traditional system with multiple colocated antennas. Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings. This letter proposes Intelligent Cell brEathing (ICE) to optimize the utilization of green energy in cellular networks by minimizing the maximal energy depleting rate of the low-power base stations powered by green energy. Minimizing the maximal depleting rate is an NP-hard problem. ICE is thus proposed to achieve low computational complexity. ICE, in each iteration, finds the energy dependent set and the vector of beacon power level decrements for low-power base stations in the set, and then shrinks the coverage area of these base stations by reducing their beacon power levels. The algorithm iterates until the optimal solution is found. ICE balances the energy consumptions among LBSs, enables more users to be served with green energy, and therefore reduces the on-grid energy consumption. Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to greening cellular networks. With the development of green energy technologies, BSs can be powered by green energy in order to reduce the on-grid power consumption, and subsequently reduce the carbon footprints. In this paper, we have proposed a distributed scheme to enable Green-energy Aware and Latency Aware (GALA) user-BS associations in heterogeneous cellular networks whose BSs are powered by both on-grid power and green energy. The GALA algorithm considers both the traffic delivery latency and the green energy generation rate in BSs for determining the user-BS association. We have proved that the GALA algorithm converges to the optimal solution which minimizes the summation of the weighted latency ratios of BSs. The simulation results show that the GALA algorithm enables the trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a significant amount of on-grid power in heterogeneous cellular networks that are powered by both on-grid power and green energy.