43,658 research outputs found
Communicating Using an Energy Harvesting Transmitter: Optimum Policies Under Energy Storage Losses
In this paper, short-term throughput optimal power allocation policies are
derived for an energy harvesting transmitter with energy storage losses. In
particular, the energy harvesting transmitter is equipped with a battery that
loses a fraction of its stored energy. Both single user, i.e. one
transmitter-one receiver, and the broadcast channel, i.e., one
transmitter-multiple receiver settings are considered, initially with an
infinite capacity battery. It is shown that the optimal policies for these
models are threshold policies. Specifically, storing energy when harvested
power is above an upper threshold, retrieving energy when harvested power is
below a lower threshold, and transmitting with the harvested energy in between
is shown to maximize the weighted sum-rate. It is observed that the two
thresholds are related through the storage efficiency of the battery, and are
nondecreasing during the transmission. The results are then extended to the
case with finite battery capacity, where it is shown that a similar
double-threshold structure arises but the thresholds are no longer monotonic. A
dynamic program that yields an optimal online power allocation is derived, and
is shown to have a similar double-threshold structure. A simpler online policy
is proposed and observed to perform close to the optimal policy.Comment: Submitted to IEEE Transactions on Wireless Communications, August
201
Receding Horizon Temporal Logic Control for Finite Deterministic Systems
This paper considers receding horizon control of finite deterministic
systems, which must satisfy a high level, rich specification expressed as a
linear temporal logic formula. Under the assumption that time-varying rewards
are associated with states of the system and they can be observed in real-time,
the control objective is to maximize the collected reward while satisfying the
high level task specification. In order to properly react to the changing
rewards, a controller synthesis framework inspired by model predictive control
is proposed, where the rewards are locally optimized at each time-step over a
finite horizon, and the immediate optimal control is applied. By enforcing
appropriate constraints, the infinite trajectory produced by the controller is
guaranteed to satisfy the desired temporal logic formula. Simulation results
demonstrate the effectiveness of the approach.Comment: Technical report accompanying a paper to be presented at ACC 201
Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches
An overview of game-theoretic approaches to energy-efficient resource
allocation in wireless networks is presented. Focusing on multiple-access
networks, it is demonstrated that game theory can be used as an effective tool
to study resource allocation in wireless networks with quality-of-service (QoS)
constraints. A family of non-cooperative (distributed) games is presented in
which each user seeks to choose a strategy that maximizes its own utility while
satisfying its QoS requirements. The utility function considered here measures
the number of reliable bits that are transmitted per joule of energy consumed
and, hence, is particulary suitable for energy-constrained networks. The
actions available to each user in trying to maximize its own utility are at
least the choice of the transmit power and, depending on the situation, the
user may also be able to choose its transmission rate, modulation, packet size,
multiuser receiver, multi-antenna processing algorithm, or carrier allocation
strategy. The best-response strategy and Nash equilibrium for each game is
presented. Using this game-theoretic framework, the effects of power control,
rate control, modulation, temporal and spatial signal processing, carrier
allocation strategy and delay QoS constraints on energy efficiency and network
capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on
Resource-Constrained Signal Processing, Communications and Networking, May
200
Jointly Optimal Spatial Channel Assignment and Power Allocation for MIMO SWIPT Systems
The joint design of spatial channel assignment and power allocation in
Multiple Input Multiple Output (MIMO) systems capable of Simultaneous Wireless
Information and Power Transfer (SWIPT) is studied. Assuming availability of
channel state information at both communications ends, we maximize the
harvested energy at the multi-antenna receiver, while satisfying a minimum
information rate requirement for the MIMO link. We first derive the globally
optimal eigenchannel assignment and power allocation design, and then present a
practically motivated tight closed-form approximation for the optimal design
parameters. Selected numerical results verify the validity of the optimal
solution and provide useful insights on the proposed designs as well as the
pareto-optimal rate-energy tradeoff.Comment: 5 pages; 4 figures; accepted to IEEE journal 201
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
A MIP framework for non-convex uniform price day-ahead electricity auctions
It is well-known that a market equilibrium with uniform prices often does not
exist in non-convex day-ahead electricity auctions. We consider the case of the
non-convex, uniform-price Pan-European day-ahead electricity market "PCR"
(Price Coupling of Regions), with non-convexities arising from so-called
complex and block orders. Extending previous results, we propose a new
primal-dual framework for these auctions, which has applications in both
economic analysis and algorithm design. The contribution here is threefold.
First, from the algorithmic point of view, we give a non-trivial exact (i.e.
not approximate) linearization of a non-convex 'minimum income condition' that
must hold for complex orders arising from the Spanish market, avoiding the
introduction of any auxiliary variables, and allowing us to solve market
clearing instances involving most of the bidding products proposed in PCR using
off-the-shelf MIP solvers. Second, from the economic analysis point of view, we
give the first MILP formulations of optimization problems such as the
maximization of the traded volume, or the minimization of opportunity costs of
paradoxically rejected block bids. We first show on a toy example that these
two objectives are distinct from maximizing welfare. We also recover directly a
previously noted property of an alternative market model. Third, we provide
numerical experiments on realistic large-scale instances. They illustrate the
efficiency of the approach, as well as the economics trade-offs that may occur
in practice
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