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Register a new userEnergy Harvesting Transmitters that Heat Up: Throughput Maximization under Temperature Constraints
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Added by
Omur Ozel
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Motivated by damage due to heating in sensor operation, we consider the throughput optimal offline data scheduling problem in an energy harvesting transmitter such that the resulting temperature increase remains below a critical level. We model the temperature dynamics of the transmitter as a linear system and determine the optimal transmit power policy under such temperature constraints as well as energy harvesting constraints over an AWGN channel. We first derive the structural properties of the solution for the general case with multiple energy arrivals. We show that the optimal power policy is piecewise monotone decreasing with possible jumps at the energy harvesting instants. We derive analytical expressions for the optimal solution in the single energy arrival case. We show that, in the single energy arrival case, the optimal power is monotone decreasing, the resulting temperature is monotone increasing, and both remain constant after the temperature hits the critical level. We then generalize the solution for the multiple energy arrival case.
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In this work, we derive optimal transmission policies in an energy harvesting status update system. The system monitors a stochastic process which can be either in a normal or in an alarm state of operation. We capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables and extend the definition of AoI to account for the state changes of the stochastic process. We formulate the problem at hand as a Markov Decision Process which, under the assumption that the demand for status updates is higher when the stochastic process is in the alarm state, utilizes a transition cost function that applies linear and non-linear penalties based on AoI and the state of the stochastic process. Finally, we evaluate numerically the derived policies and illustrate their effectiveness for reserving energy in anticipation of future alarm states.
The paper investigates the problem of maximizing expected sum throughput in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of sum-capacity of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables namely spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU textit{transmission power} and textit{sensing time}, which is non-convex in nature. We solve the resulting optimization problem as an alternating convex optimization problem for both non-causal and causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC). We present an analytic solution for the non-causal scenario with infinite battery capacity for a general finite horizon problem.We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.
This work considers an additive noise channel where the time-k noise variance is a weighted sum of the channel input powers prior to time k. This channel is motivated by point-to-point communication between two terminals that are embedded in the same chip. Transmission heats up the entire chip and hence increases the thermal noise at the receiver. The capacity of this channel (both with and without feedback) is studied at low transmit powers and at high transmit powers. At low transmit powers, the slope of the capacity-vs-power curve at zero is computed and it is shown that the heating-up effect is beneficial. At high transmit powers, conditions are determined under which the capacity is bounded, i.e., under which the capacity does not grow to infinity as the allowed average power tends to infinity.
In this paper, we propose a multiple-input multipleoutput (MIMO) transmission strategy that is closer to the Shannon limit than the existing strategies. Different from most existing strategies which only consider uniformly distributed discrete input signals, we present a unified framework to optimize the MIMO precoder and the discrete input signal distribution jointly. First, a general model of MIMO transmission under discrete input signals and its equivalent formulation are established. Next, in order to maximize the mutual information between the input and output signals, we provide an algorithm that jointly optimizes the precoder and the input distribution. Finally, we compare our strategy with other existing strategies in the simulation. Numerical results indicate that our strategy narrows the gap between the mutual information and Shannon limit, and shows a lower frame error rate in simulation.
Age of Information (AoI) is a newly appeared concept and metric to characterize the freshness of data. In this work, we study the delay and AoI in a multiple access channel (MAC) with two source nodes transmitting different types of data to a common destination. The first node is grid-connected and its data packets arrive in a bursty manner, and at each time slot it transmits one packet with some probability. Another energy harvesting (EH) sensor node generates a new status update with a certain probability whenever it is charged. We derive the delay of the grid-connected node and the AoI of the EH sensor as functions of different parameters in the system. The results show that the mutual interference has a non-trivial impact on the delay and age performance of the two nodes.
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