4,423 research outputs found
Renewable Energy Distribution in Cooperative Cellular Networks with Energy Harvesting
In this paper, we propose a novel online centralized
algorithm for energy cooperation among energy harvesting capable
base stations (BSs) in multi-tier cellular networks. BSs are connected
to the non-renewable source used by a BS when it cannot
harvest sufficient energy to serve its connected users. BSs with
the extra harvested energy operate cooperatively and share their
surplus energy with BSs that have not harvested sufficient energy.
To stimulate BSs with energy deficit to use the shared energy
of other BSs, an energy pricing framework is established which
results in reducing of the non-renewable energy consumption. We
formulate the problem of maximizing the fairness of the renewable
energy distribution. The closed-form of energy share given to
each BS with energy deficit is found, by which the renewable
energy distribution fairness is maximized. Energy is shared by
the smart grid. The problem of minimizing the smart grid usage
cost for distributing energy is formulated and an online algorithm
is proposed to approximate its solution. Simulation results show
that the approximate algorithm reduces the non-renewable energy
consumption significantly and reduces the cost of smart grid usage
near to the optimal solution
Cooperative Energy Trading in CoMP Systems Powered by Smart Grids
This paper studies the energy management in the coordinated multi-point
(CoMP) systems powered by smart grids, where each base station (BS) with local
renewable energy generation is allowed to implement the two-way energy trading
with the grid. Due to the uneven renewable energy supply and communication
energy demand over distributed BSs as well as the difference in the prices for
their buying/selling energy from/to the gird, it is beneficial for the
cooperative BSs to jointly manage their energy trading with the grid and energy
consumption in CoMP based communication for reducing the total energy cost.
Specifically, we consider the downlink transmission in one CoMP cluster by
jointly optimizing the BSs' purchased/sold energy units from/to the grid and
their cooperative transmit precoding, so as to minimize the total energy cost
subject to the given quality of service (QoS) constraints for the users. First,
we obtain the optimal solution to this problem by developing an algorithm based
on techniques from convex optimization and the uplink-downlink duality. Next,
we propose a sub-optimal solution of lower complexity than the optimal
solution, where zero-forcing (ZF) based precoding is implemented at the BSs.
Finally, through extensive simulations, we show the performance gain achieved
by our proposed joint energy trading and communication cooperation schemes in
terms of energy cost reduction, as compared to conventional schemes that
separately design communication cooperation and energy trading
Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks
Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings
Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage
In this paper, we consider delay minimization for interference networks with
renewable energy source, where the transmission power of a node comes from both
the conventional utility power (AC power) and the renewable energy source. We
assume the transmission power of each node is a function of the local channel
state, local data queue state and local energy queue state only. In turn, we
consider two delay optimization formulations, namely the decentralized
partially observable Markov decision process (DEC-POMDP) and Non-cooperative
partially observable stochastic game (POSG). In DEC-POMDP formulation, we
derive a decentralized online learning algorithm to determine the control
actions and Lagrangian multipliers (LMs) simultaneously, based on the policy
gradient approach. Under some mild technical conditions, the proposed
decentralized policy gradient algorithm converges almost surely to a local
optimal solution. On the other hand, in the non-cooperative POSG formulation,
the transmitter nodes are non-cooperative. We extend the decentralized policy
gradient solution and establish the technical proof for almost-sure convergence
of the learning algorithms. In both cases, the solutions are very robust to
model variations. Finally, the delay performance of the proposed solutions are
compared with conventional baseline schemes for interference networks and it is
illustrated that substantial delay performance gain and energy savings can be
achieved
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