17-06-2014, 10:28 AM
Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station
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Abstract
We study resource allocation algorithm design for
energy-efficient communication in an orthogonal frequency division
multiple access (OFDMA) downlink network with hybrid
energy harvesting base station (BS). 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 over a finite horizon 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 exploiting the properties of non-linear
fractional programming which results in an efficient asymptotically
optimal offline iterative resource allocation algorithm for
a sufficiently large number of subcarriers. 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 (weighted
bit/Joule delivered to the receiver). Subsequently, we focus on
online algorithm design. A conventional stochastic dynamic
programming approach is employed to obtain the optimal online
resource allocation algorithm which entails 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 algorithm. Simulation results illustrate that the proposed
suboptimal online iterative resource allocation algorithm does not
only converge in a small number of iterations, but also achieves a
close-to-optimal system energy efficiency by utilizing only causal
channel state and energy arrival information.
INTRODUCTION
1], [2]. Specifically, OFDMA converts a wideband channel
into a number of orthogonal narrowband subcarrier channels
and multiplexes the data of multiple users on different subcarriers.
In a downlink OFDMA system, the maximum system
throughput can be achieved by selecting the best user on each
subcarrier and adapting the transmit power over all subcarriers
using water-filling. On the other hand, the increasing interest
in high data rate services such as video conferencing and online
high definition video streaming has led to a high demand
for energy. This trend has significant financial implications
for service providers due to the rapidly increasing cost of
energy. Recently, driven by environmental concerns, green
communication has received considerable interest from both
industry and academia [3]-[6]. In fact, the cellular networks
consume world-wide approximately 60 billion kWh per year.
In particular, 80% of the electricity in cellular networks is
consumed by the base stations (BSs) which produce over a
hundred million tons of carbon dioxide per year [6]. These
figures are projected to double by the year 2020 if no further
actions are taken. As a result, a tremendous number of green
technologies/methods have been proposed in the literature for
maximizing the energy efficiency (bit-per-
OFDMA SYSTEM MODEL
A. Notation
A complex Gaussian random variable with mean μ and
variance σ2 is denoted by CN(μ, σ2), and ∼ means “distributed
as”.
x
+
= max{0, x}.
x
a
b = a, if x > a,
x
a
b =
x, if b ≤ x ≤ a,
x
a
b = b, if b > x. Ex{·} denotes statistical
expectation with respect to (w.r.t.) random variable x.
RESULTS AND DISCUSSIONS
In this section, we evaluate the system performance for
the proposed resource allocation and scheduling algorithms
using simulations. A micro-cell system with radius 500 m
is considered. The number of subcarriers is nF = 128 with
carrier center frequency 2.5 GHz, system bandwidth B = 5
MHz, and αk = 1, ∀k. Each subcarrier has a bandwidth of 39
kHz and the noise variance is σ2
z = −128 dBm. The 3GPP
urban path loss model is used [42] with a reference distance
of d0 = 35 m. The K desired users are uniformly distributed
between the reference distance and the cell boundary. The
small scale fading coefficients of the BS-to-user links are
generated as independent and identically distributed (i.i.d.)
Rayleigh random variables. The multipath channel characteristic
of each user is assumed to follow the power delay profile
according of the LTE extended pedestrian A channel model
[43]. The static circuit power consumption is set to P
CONCLUSIONS
which the circuit energy consumption, the finite battery storage
capacity, and a minimum system data rate requirement were
taken into consideration. We first studied the structure of the
asymptotically optimal offline resource allocation algorithm
by assuming non-causal channel gain and energy arrival
knowledge. Then, the derived offline solution served as a
building block for the design of a practical close-to-optimal
online resource allocation algorithm requiring only causal
system knowledge. Simulation results did not only unveil the
achievable maximum weighted energy efficiency, but showed
also that the proposed suboptimal online algorithm achieves
a close-to-optimal performance within a small number of
iterations. Interesting topics for future work include studying
the effects of imperfect CSI and energy leakage.