5,211 research outputs found
Energy-Efficient Antenna Selection and Power Allocation for Large-Scale Multiple Antenna Systems with Hybrid Energy Supply
The combination of energy harvesting and large-scale multiple antenna
technologies provides a promising solution for improving the energy efficiency
(EE) by exploiting renewable energy sources and reducing the transmission power
per user and per antenna. However, the introduction of energy harvesting
capabilities into large-scale multiple antenna systems poses many new
challenges for energy-efficient system design due to the intermittent
characteristics of renewable energy sources and limited battery capacity.
Furthermore, the total manufacture cost and the sum power of a large number of
radio frequency (RF) chains can not be ignored, and it would be impractical to
use all the antennas for transmission. In this paper, we propose an
energy-efficient antenna selection and power allocation algorithm to maximize
the EE subject to the constraint of user's quality of service (QoS). An
iterative offline optimization algorithm is proposed to solve the non-convex EE
optimization problem by exploiting the properties of nonlinear fractional
programming. The relationships among maximum EE, selected antenna number,
battery capacity, and EE-SE tradeoff are analyzed and verified through computer
simulations.Comment: IEEE Globecom 2014 Selected Areas in Communications Symposium-Green
Communications and Computing Trac
On Green Energy Powered Cognitive Radio Networks
Green energy powered cognitive radio (CR) network is capable of liberating
the wireless access networks from spectral and energy constraints. The
limitation of the spectrum is alleviated by exploiting cognitive networking in
which wireless nodes sense and utilize the spare spectrum for data
communications, while dependence on the traditional unsustainable energy is
assuaged by adopting energy harvesting (EH) through which green energy can be
harnessed to power wireless networks. Green energy powered CR increases the
network availability and thus extends emerging network applications. Designing
green CR networks is challenging. It requires not only the optimization of
dynamic spectrum access but also the optimal utilization of green energy. This
paper surveys the energy efficient cognitive radio techniques and the
optimization of green energy powered wireless networks. Existing works on
energy aware spectrum sensing, management, and sharing are investigated in
detail. The state of the art of the energy efficient CR based wireless access
network is discussed in various aspects such as relay and cooperative radio and
small cells. Envisioning green energy as an important energy resource in the
future, network performance highly depends on the dynamics of the available
spectrum and green energy. As compared with the traditional energy source, the
arrival rate of green energy, which highly depends on the environment of the
energy harvesters, is rather random and intermittent. To optimize and adapt the
usage of green energy according to the opportunistic spectrum availability, we
discuss research challenges in designing cognitive radio networks which are
powered by energy harvesters
A Lyapunov Optimization Approach for Green Cellular Networks with Hybrid Energy Supplies
Powering cellular networks with renewable energy sources via energy
harvesting (EH) has recently been proposed as a promising solution for green
networking. However, with intermittent and random energy arrivals, it is
challenging to provide satisfactory quality of service (QoS) in EH networks. To
enjoy the greenness brought by EH while overcoming the instability of the
renewable energy sources, hybrid energy supply (HES) networks that are powered
by both EH and the electric grid have emerged as a new paradigm for green
communications. In this paper, we will propose new design methodologies for HES
green cellular networks with the help of Lyapunov optimization techniques. The
network service cost, which addresses both the grid energy consumption and
achievable QoS, is adopted as the performance metric, and it is optimized via
base station assignment and power control (BAPC). Our main contribution is a
low-complexity online algorithm to minimize the long-term average network
service cost, namely, the Lyapunov optimization-based BAPC (LBAPC) algorithm.
One main advantage of this algorithm is that the decisions depend only on the
instantaneous side information without requiring distribution information of
channels and EH processes. To determine the network operation, we only need to
solve a deterministic per-time slot problem, for which an efficient inner-outer
optimization algorithm is proposed. Moreover, the proposed algorithm is shown
to be asymptotically optimal via rigorous analysis. Finally, sample simulation
results are presented to verify the theoretical analysis as well as validate
the effectiveness of the proposed algorithm.Comment: 15 pages, 8 figures, to appear in IEEE Journal on Selected Areas in
Communication
Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy
The application of renewable energy is a promising solution to realize the
Green Communications. However, if the cellular systems are solely powered by
the renewable energy, the weather dependence of the renewable energy arrival
makes the systems unstable. On the other hand, the proliferation of the smart
grid facilitates the loads with two-way energy trading capability. Hence, a
hybrid powered cellular system, which combines the smart grid with the base
stations, can reduce the grid energy expenditure and improve the utilization
efficiency of the renewable energy. In this paper, the long-term grid energy
expenditure minimization problem is formulated as a stochastic optimization
model. By leveraging the stochastic optimization theory, we reformulate the
stochastic optimization problem as a \mbox{per-frame} grid energy plus weighted
penalized packet rate minimization problem, which is NP-hard. As a result, two
suboptimal algorithms, which jointly consider the effects of the channel
quality and the packet reception failure, are proposed based on the successive
approximation beamforming (SABF) technique and the \mbox{zero-forcing}
beamforming (ZFBF) technique. The convergence properties of the proposed
suboptimal algorithms are established, and the corresponding computational
complexities are analyzed. Simulation results show that the proposed SABF
algorithm outperforms the ZFBF algorithm in both grid energy expenditure and
packet delay. By tuning a control parameter, the grid energy expenditure can be
traded for the packet delay under the proposed stochastic optimization model.Comment: accepted by IEEE Trans. Wireless Commu
Wireless Information and Power Transfer Design for Energy Cooperation Distributed Antenna Systems
Distributed antenna systems (DAS) have been widely implemented in
state-of-the-art cellular communication systems to cover dead spots. Recent
studies have also indicated that DAS have advantages in wireless energy
transfer (WET). In this paper, we study simultaneous wireless information and
power transfer (SWIPT) for a multiple-input single-output (MISO) DAS in the
downlink which consists of arbitrarily distributed remote antenna units (RAUs).
In order to save the energy cost, we adopt energy cooperation of energy
harvesting (EH) and two-way energy flows to let the RAUs trade their harvested
energy through the smart grid network. Under individual EH constraints, per-RAU
power constraints and various smart grid considerations, we investigate a power
management strategy that determines how to utilize the stochastically spatially
distributed harvested energy at the RAUs and how to trade the energy with the
smart grid simultaneously to supply maximum wireless information transfer (WIT)
with a minimum WET constraint for a receiver adopting power splitting (PS). Our
analysis shows that the optimal design can be achieved in two steps. The first
step is to maximize a new objective that can simultaneously maximize both WET
and WIT, considering both the smart grid profitable and smart grid neutral
cases. For the grid-profitable case, we derive the optimal full power strategy
and provide a closed-form result to see under what condition this strategy is
used. On the other hand, for the grid-neutral case, we illustrate that the
optimal power policy has a double-threshold structure and present an optimal
allocation strategy. The second step is then to solve the whole problem by
obtaining the splitting power ratio based on the minimum WET constraint.
Simulation results are provided to evaluate the performance under various
settings and characterize the double-threshold structure.Comment: 11 pages, 7 figure
Ambient RF Energy Harvesting in Ultra-Dense Small Cell Networks: Performance and Trade-offs
In order to minimize electric grid power consumption, energy harvesting from
ambient RF sources is considered as a promising technique for wireless charging
of low-power devices. To illustrate the design considerations of RF-based
ambient energy harvesting networks, this article first points out the primary
challenges of implementing and operating such networks, including
non-deterministic energy arrival patterns, energy harvesting mode selection,
energy-aware cooperation among base stations (BSs), etc. A brief overview of
the recent advancements and a summary of their shortcomings are then provided
to highlight existing research gaps and possible future research directions. To
this end, we investigate the feasibility of implementing RF-based ambient
energy harvesting in ultra-dense small cell networks (SCNs) and examine the
related trade-offs in terms of the energy efficiency and
signal-to-interference-plus-noise ratio (SINR) outage probability of a typical
user in the downlink. Numerical results demonstrate the significance of
deploying a mixture of on-grid small base stations (SBSs)~(powered by electric
grid) and off-grid SBSs~(powered by energy harvesting) and optimizing their
corresponding proportions as a function of the intensity of active SBSs in the
network.Comment: IEEE Wireless Communications, to appea
FreeNet: Spectrum and Energy Harvesting Wireless Networks
The dramatic mobile data traffic growth is not only resulting in the spectrum
crunch but is also leading to exorbitant energy consumption. It is thus
desirable to liberate mobile and wireless networks from the constraint of the
spectrum scarcity and to rein in the growing energy consumption. This article
introduces FreeNet, figuratively synonymous to "Free Network", which engineers
the spectrum and energy harvesting techniques to alleviate the spectrum and
energy constraints by sensing and harvesting spare spectrum for data
communications and utilizing renewable energy as power supplies, respectively.
Hence, FreeNet increases the spectrum and energy efficiency of wireless
networks and enhances the network availability. As a result, FreeNet can be
deployed to alleviate network congestion in urban areas, provision broadband
services in rural areas, and upgrade emergency communication capacity. This
article provides a brief analysis of the design of FreeNet that accommodates
the dynamics of the spare spectrum and employs renewable energy
Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station
We study resource allocation algorithm design for energy-efficient
communication in an OFDMA downlink network with hybrid energy harvesting base
station. Specifically, an energy harvester and a constant energy source driven
by a non-renewable resource are used for supplying the energy required for
system operation. We first consider a deterministic offline system setting. In
particular, assuming availability of non-causal knowledge about energy arrivals
and channel gains, an offline resource allocation problem is formulated as a
non-convex optimization problem taking into account the circuit energy
consumption, a finite energy storage capacity, and a minimum required data
rate. We transform this non-convex optimization problem into a convex
optimization problem by applying time-sharing and fractional programming which
results in an efficient asymptotically optimal offline iterative resource
allocation algorithm. In each iteration, the transformed problem is solved by
using Lagrange dual decomposition. The obtained resource allocation policy
maximizes the weighted energy efficiency of data transmission. Subsequently, we
focus on online algorithm design. A stochastic dynamic programming approach is
employed to obtain the optimal online resource allocation algorithm which
requires a prohibitively high complexity. To strike a balance between system
performance and computational complexity, we propose a low complexity
suboptimal online iterative algorithm which is motivated by the offline
optimization.Comment: 32 pages, 7 figures, and 1 table. Submitted for possible journal
publication in 201
Dynamic Base Station Operation in Large-Scale Green Cellular Networks
In this paper, to minimize the on-grid energy cost in a large-scale green
cellular network, we jointly design the optimal base station (BS) on/off
operation policy and the on-grid energy purchase policy from a network-level
perspective. Due to the fluctuations of the on-grid energy prices, the
harvested renewable energy, and the network traffic loads over time, as well as
the BS coordination to hand over the traffic offloaded from the inactive BSs to
the active BSs, it is generally NP-hard to find a network-level optimal
adaptation policy that can minimize the on-grid energy cost over a long-term
and yet assures the downlink transmission quality at the same time. Aiming at
the network-level dynamic system design, we jointly apply stochastic geometry
(Geo) for large-scale green cellular network analysis and dynamic programming
(DP) for adaptive BS on/off operation design and on-grid energy purchase
design, and thus propose a new Geo-DP design approach. By this approach, we
obtain the optimal BS on/off policy, which shows that the optimal BSs' active
operation probability in each horizon is just sufficient to assure the required
downlink transmission quality with time-varying load in the large-scale
cellular network. We also propose a suboptimal on-grid energy purchase policy
with low-complexity, where the low-price on-grid energy is over-purchased in
the current horizon only when the current storage level and the future
renewable energy level are both low. We compare the proposed policy with the
existing schemes and show that our proposed policy can more efficiently save
the on-grid energy cost over time.Comment: Submitted for possible journal publication. 29 pages, 6 figures, and
1 tabl
Secure and Green SWIPT in Distributed Antenna Networks with Limited Backhaul Capacity
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 (RF)
signals. The considered resource allocation algorithm design is formulated as a
mixed non-convex 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. Besides, the distributed antenna network for
SWIPT with renewable energy sharing is shown to require a lower transmit power
compared to a traditional system with multiple co-located antennas.Comment: accepted for publication, IEEE Transactions on Wireless
Communications, May 10, 201
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