Abstract-We address the problem of power allocation to maximize ergodic capacity subject to multiple power constraints assuming that perfect causal channel state information (CSI) is available at both the transmitter and the receiver. We characterize the optimal power allocation subject to both long-term and short-term power constraints, which depends upon the ratio of the two power levels. Additionally, we find a suboptimal power allocation if the input power is subject to long-term and per-antenna power constraints. We characterize the conditions for which one power constraint dominates and the other can be ignored. Numerical results suggest that, for the Rayleigh fading case, a short-term power constraint that is larger than a long-term power constraint does not significantly impact the ergodic capacity of the channel. The effect of per-antenna power constraints is also explored for the case of Rayleigh fading through our numerical results.
Abstract-We consider several fading channel models for which we aim to maximize ergodic capacity assuming that channel state information (CSI) is available at both the receiver and the transmitter. We characterize the optimal power allocation structure in the single-input-single-output (SISO), multipleinput-single-output (MISO), and multiple-input-multiple-output (MIMO) models subject to both long-term and short-term power constraints. The optimal power policy in each of the channel models depends upon the ratio of the two power constraints and the average signal-to-noise-ratio (SNR) of the system. We characterize the conditions, in which the short-term power constraint can be eliminated without being violated in the optimal power policy. Numerical results in the Rayleigh fading MISO case shows that the value of the short-term power constraint does not greatly affect the ergodic capacity of the channel for large values of average SNRs, as long as it is greater than the value of the long-term power constraint.
Abstract-We consider an energy harvesting wireless system where data is stored in a data buffer and energy is harvested from various sources and stored in an energy buffer. The transmitter tries to minimize a delay criterion, e.g., average delay in the data buffer or probability of overflow, subject to the hard power constraints imposed by the available energy. We show that if the average rate of data arrival is larger than a certain threshold, then the probability of overflow is lower bounded by a constant and the average delay is infinite, and if it is smaller than this threshold, then there exists a sequence of simple policies such that the probability of overflow goes to zero faster than 1/L K for any K ≥ 2, where L is the length of the data buffer, and average delay is upper bounded by some constants. Furthermore, we extend the results into a multiple access channel (MAC) with two users, and study two situations, when the energy buffers of the two users are separate, and when the energy buffers of the two users can cooperate. I. INTRODUCTION AND OVERVIEWIn energy harvesting wireless systems, the users communicate with each other in a fading environment and they harvest energy from a variety of sources and store it in an energy buffer such as capacitor or battery. Energy harvesting has attracted considerable attention recently due to its cheap, green, renewable, and naturally-presented source of energy. Solar, thermal, kinetic, and wind are examples of such sources of energy. Energy harvesting systems have a well-known application in powering low-energy electronics for wireless sensor networks.In the described system, the question then naturally arises how we should spend the harvested energy and what is the objective function. Delay constraints are important limitations in wireless communications. Therefore, one might optimize over all power policies in order to minimize a delay criterion. The authors in [1] consider a fading channel between two users and assume that the data arrives randomly due to a data arrival distribution and stores in a data buffer until it is transmitted. In that paper, there are two performance criteria to be traded off: the long-term average power consumption and the average delay to send the data. The authors in [2] consider an energy harvesting communication system over a constant link. They assume an ideal situation in which the exact amount of data and energy arrivals is known to the transmitter apriority, and develop an off-line algorithm to minimize the transmission time of all the data up to a certain time. This model allows for relatively simple and explicit analysis by
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