612 research outputs found
Online Power Control for Block i.i.d. Energy Harvesting Channels
We study the problem of online power control for energy harvesting
communication nodes with random energy arrivals and a finite battery. We assume
a block i.i.d. stochastic model for the energy arrivals, in which the energy
arrivals are constant for a fixed duration , but are independent across
different blocks, drawn from an arbitrary distribution. This model serves as a
simple approximation to a random process with coherence time . We propose a
simple online power control policy, and prove that its performance gap to the
optimal throughput is bounded by a constant which is independent of the
parameters of the problem. This also yields a simple formula for the
approximately optimal long-term average throughput, which sheds some light on
the qualitative behavior of the throughput and how it depends on the coherence
time of the energy arrival process. Our results show that, perhaps
counter-intuitively, for a fixed mean energy arrival rate the throughput
decreases with increasing coherence time of the energy arrival process. In
particular, the battery size needed to approach the AWGN capacity of the
channel increases linearly with the coherence time of the process. Finally, we
show that our results can provide an approximation to the information-theoretic
capacity of the same channel.Comment: submitted to IEEE Transactions on Information Theor
Power Management Policies for AWGN Channels with Slow-Varying Harvested Energy
In this paper, we study power management (PM) policies for an Energy
Harvesting Additive White Gaussian Noise (EH-AWGN) channel. The arrival rate of
the harvested energy is assumed to remain unchanged during each data frame
(block code) and to change independently across block codes. The harvested
energy sequence is known causally (online) at the transmitter. The transmitter
is equipped with a rechargeable battery with infinite energy storage capacity.
The transmitter is able to adapt the allocated energy and the corresponding
transmission rate of each block according to a PM policy. Three novel online PM
policies are established. The policies are universal, in the sense of the
distribution of the harvested energy, and simple, in the sense of complexity,
and asymptotically optimal, in the sense of maximum achievable average rates
(throughput) taken over a long-term horizon of blocks.Comment: 4 Figures, 6 page
Capacity of the Energy Harvesting Channel with a Finite Battery
We consider an energy harvesting channel, in which the transmitter is powered
by an exogenous stochastic energy harvesting process , such that , which can be stored in a battery of finite size . We
provide a simple and insightful formula for the approximate capacity of this
channel with bounded guarantee on the approximation gap independent of system
parameters. This approximate characterization of the capacity identifies two
qualitatively different operating regimes for this channel: in the large
battery regime, when , the capacity is approximately equal
to that of an AWGN channel with an average power constraint equal to the
average energy harvesting rate, i.e. it depends only on the mean of and
is (almost) independent of the distribution of and the exact value of
. In particular, this suggests that a battery size
is approximately sufficient to extract the infinite
battery capacity of the system. In the small battery regime, when
, we clarify the dependence of the capacity on the
distribution of and the value of . There are three steps to
proving this result which can be of interest in their own right: 1) we
characterize the capacity of this channel as an -letter mutual information
rate under various assumptions on the availability of energy arrival
information; 2) we characterize the approximately optimal online power control
policy that maximizes the long-term average throughput of the system; 3) we
show that the information-theoretic capacity of this channel is equal, within a
constant gap, to its long-term average throughput. This last result provides a
connection between the information- and communication-theoretic formulations of
the energy-harvesting communication problem that have been so far studied in
isolation.Comment: submitted to IEEE Transactions on Information Theory; revised
following reviewers' comment
SWIPT using Hybrid ARQ over Time Varying Channels
We consider a class of wireless powered devices employing Hybrid Automatic
Repeat reQuest (HARQ) to ensure reliable end-to-end communications over a
two-state time-varying channel. A receiver, with no power source, relies on the
energy transferred by a Simultaneous Wireless Information and Power Transfer
(SWIPT) enabled transmitter to \emph{receive} and \emph{decode} information.
Under the two-state channel model, information is received at two different
rates while it is only possible to harvest energy in one of the states. The
receiver aims to decode its messages with minimum expected number of
re-transmissions. Dynamic and continuous nature of the problem motivated us to
use a novel Markovian framework to bypass the complexities plaguing the
conventional approaches such as MDP. Using the theory of absorbing Markov
chains, we show that there exists an optimal policy utilizing the incoming RF
signal solely to harvest energy or to accumulate mutual information. Hence, we
convert the original problem with continuous action and state space into an
equivalent one with discrete state and action space. For independent and
identically distributed channels, we prove the optimality of a
simple-to-implement {\em harvest-first-store-later} type policy. However, for
time-correlated channels, we demonstrate that statistical knowledge of the
channel may significantly improve the performance over such policies
Closed-Form Delay-Optimal Power Control for Energy Harvesting Wireless System with Finite Energy Storage
In this paper, we consider delay-optimal power control for an energy
harvesting wireless system with finite energy storage. The wireless system is
powered solely by a renewable energy source with bursty data arrivals, and is
characterized by a data queue and an energy queue. We consider a delay-optimal
power control problem and formulate an infinite horizon average cost Markov
Decision Process (MDP). To deal with the curse of dimensionality, we introduce
a virtual continuous time system and derive closed-form approximate priority
functions for the discrete time MDP at various operating regimes. Based on the
approximation, we obtain an online power control solution which is adaptive to
the channel state information as well as the data and energy queue state
information. The derived power control solution has a multi-level water-filling
structure, where the water level is determined jointly by the data and energy
queue lengths. We show through simulations that the proposed scheme has
significant performance gain compared with various baselines.Comment: 17 pages, 9 figures, 1 table. Accepted for publication in IEEE
Transactions on Signal Processin
Optimal Power Allocation for Outage Minimization in Fading Channels with Energy Harvesting Constraints
This paper studies the optimal power allocation for outage minimization in
point-to-point fading channels with the energy-harvesting constraints and
channel distribution information (CDI) at the transmitter. Both the cases with
non-causal and causal energy state information (ESI) are considered, which
correspond to the energy harvesting rates being known and unknown prior to the
transmissions, respectively. For the non-causal ESI case, the average outage
probability minimization problem over a finite horizon is shown to be
non-convex for a large class of practical fading channels. However, the
globally optimal "offline" power allocation is obtained by a forward search
algorithm with at most one-dimensional searches, and the optimal power
profile is shown to be non-decreasing over time and have an interesting
"save-then-transmit" structure. In particular, for the special case of N=1, our
result revisits the classic outage capacity for fading channels with uniform
power allocation. Moreover, for the case with causal ESI, we propose both the
optimal and suboptimal "online" power allocation algorithms, by applying the
technique of dynamic programming and exploring the structure of optimal offline
solutions, respectively.Comment: submitted for possible Journal publicatio
Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks
Hybrid energy supply (HES) wireless networks have recently emerged as a new
paradigm to enable green networks, which are powered by both the electric grid
and harvested renewable energy. In this paper, we will investigate two critical
but conflicting design objectives of HES networks, i.e., the grid energy
consumption and quality of service (QoS). Minimizing grid energy consumption by
utilizing the harvested energy will make the network environmentally friendly,
but the achievable QoS may be degraded due to the intermittent nature of energy
harvesting. To investigate the tradeoff between these two aspects, we introduce
the total service cost as the performance metric, which is the weighted sum of
the grid energy cost and the QoS degradation cost. Base station assignment and
power control is adopted as the main strategy to minimize the total service
cost, while both cases with non-causal and causal side information are
considered. With non-causal side information, a Greedy Assignment algorithm
with low complexity and near-optimal performance is proposed. With causal side
information, the design problem is formulated as a discrete Markov decision
problem. Interesting solution structures are derived, which shall help to
develop an efficient monotone backward induction algorithm. To further reduce
complexity, a Look-Ahead policy and a Threshold-based Heuristic policy are also
proposed. Simulation results shall validate the effectiveness of the proposed
algorithms and demonstrate the unique grid energy consumption and QoS tradeoff
in HES networks.Comment: 14 pages, 7 figures, to appear in IEEE Transactions on Wireless
Communication
Distributed Opportunistic Scheduling for Energy Harvesting Based Wireless Networks: A Two-Stage Probing Approach
This paper considers a heterogeneous ad hoc network with multiple
transmitter-receiver pairs, in which all transmitters are capable of harvesting
renewable energy from the environment and compete for one shared channel by
random access. In particular, we focus on two different scenarios: the constant
energy harvesting (EH) rate model where the EH rate remains constant within the
time of interest and the i.i.d. EH rate model where the EH rates are
independent and identically distributed across different contention slots. To
quantify the roles of both the energy state information (ESI) and the channel
state information (CSI), a distributed opportunistic scheduling (DOS) framework
with two-stage probing and save-then-transmit energy utilization is proposed.
Then, the optimal throughput and the optimal scheduling strategy are obtained
via one-dimension search, i.e., an iterative algorithm consisting of the
following two steps in each iteration: First, assuming that the stored energy
level at each transmitter is stationary with a given distribution, the expected
throughput maximization problem is formulated as an optimal stopping problem,
whose solution is proved to exist and then derived for both models; second, for
a fixed stopping rule, the energy level at each transmitter is shown to be
stationary and an efficient iterative algorithm is proposed to compute its
steady-state distribution. Finally, we validate our analysis by numerical
results and quantify the throughput gain compared with the best-effort delivery
scheme.Comment: 14 pages, 5 figures, accepted by IEEE/ACM Transactions on Networkin
On Lossy Joint Source-Channel Coding In Energy Harvesting Communication Systems
We study the problem of lossy joint source-channel coding in a single-user
energy harvesting communication system with causal energy arrivals and the
energy storage unit may have leakage. In particular, we investigate the
achievable distortion in the transmission of a single source via an energy
harvesting transmitter over a point-to-point channel. We consider an adaptive
joint source-channel coding system, where the length of channel codewords
varies based on the available battery charge. We first establish a lower bound
on the achievable distortion. Then, as necessary conditions for local
optimality, we obtain two coupled equations that determine the mismatch ratio
between channel symbols and source symbols as well as the transmission power,
both as functions of the battery charge. As examples of continuous and discrete
sources, we consider Gaussian and binary sources respectively. For the Gaussian
case, we obtain a closed-form expression for the mismatch factor in terms of
the function, and show that an increasing transmission power policy
results in a decreasing mismatch factor policy and vice versa. Finally, we
numerically compare the performance of the adaptive mismatch factor scheme to
the case of a constant mismatch factor.Comment: 15 pages, 7 figures. To be published in IEEE Transactions on
Communication
Opportunistic Multi-Channel Access in Heterogeneous 5G Network with Renewable Energy Supplies
A heterogeneous system, where small networks (e.g., small cell or WiFi) boost
the system throughput under the umbrella of a large network (e.g., large cell),
is a promising architecture for the 5G wireless communication networks, where
green and sustainable communication is also a key aspect. Renewable energy
based communication via energy harvesting (EH) devices is one of such green
technology candidates. In this paper, we study an uplink transmission scenario
under a heterogeneous network hierarchy, where each mobile user (MU) is powered
by a sustainable energy supply, capable of both deterministic access to the
large network via one private channel, and dynamic access to a small network
with certain probability via one common channel shared by multiple MUs.
Considering a general EH model, i.e., energy arrivals are time-correlated, we
study an opportunistic transmission scheme and aim to maximize the average
throughput for each MU, which jointly exploits the statistics and current
states of the private channel, common channel, battery level, and EH rate.
Applying a simple yet efficient "save-then-transmit" scheme, the throughput
maximization problem is cast as a "rate-of-return" optimal stopping problem.
The optimal stopping rule is proved to has a time-dependent threshold-based
structure for the case with general Markovian system dynamics, and degrades to
a pure threshold policy for the case with independent and identically
distributed system dynamics. As performance benchmarks, the optimal power
allocation scheme with conventional power supplies is also examined. Finally,
numerical results are presented, and a new concept of "EH diversity" is
discussed.Comment: 28 pages, 8 figure
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