Ayca Ozcelikkale

Linear Precoder Design for Simultaneous Information and Energy Transfer Over Two-User MIMO Interference Channels

Communication strategies that utilize wireless media for simultaneous information and power transfer offer a promising perspective for efficient usage of energy resources. With this motivation, we focus on the design of optimal linear precoders for interference channels utilizing such strategies. We formulate the problem of minimizing the total minimum mean-square error while keeping the energy harvested at the energy receivers above given levels. Our framework leads to a non-convex problem formulation. For point-to-point multiple-input multiple-output channels, we provide a characterization of the optimal solutions under a constraint on the number of transmit antennas. For the general interference scenario, we propose two numerical approaches, one for the single antenna information receivers case, and the other for the general case. We also investigate a hybrid signalling scheme, where the transmitter sends a superposition of two signals: a deterministic signal optimized for energy transfer and an information carrying signal optimized for information and energy transfer. It is illustrated that if hybrid signalling is not incorporated into the transmission scheme, interference can be detrimental to the system performance when the number of antennas at the receivers is low.

Unitary Precoding and Basis Dependency of MMSE Performance for Gaussian Erasure Channels

We consider the transmission of a Gaussian vector source over a multidimensional Gaussian channel where a random or a fixed subset of the channel outputs are erased. Within the setup where the only encoding operation allowed is a linear unitary transformation on the source, we investigate the minimum mean-square error (MMSE) performance, both in average, and also in terms of guarantees that hold with high probability as a function of the system parameters. Under the performance criterion of average MMSE, necessary conditions that should be satisfied by the optimal unitary encoders are established and explicit solutions for a class of settings are presented. For random sampling of signals that have a low number of degrees of freedom, we present MMSE bounds that hold with high probability. Our results illustrate how the spread of the eigenvalue distribution and the unitary transformation contribute to these performance guarantees. The performance of the discrete Fourier transform (DFT) is also investigated. As a benchmark, we investigate the equidistant sampling of circularly wide-sense stationary signals, and present the explicit error expression that quantifies the effects of the sampling rate and the eigenvalue distribution of the covariance matrix of the signal. These findings may be useful in understanding the geometric dependence of signal uncertainty in a stochastic process. In particular, unlike information theoretic measures such as entropy, we highlight the basis dependence of uncertainty in a signal with another perspective. The unitary encoding space restriction exhibits the most and least favorable signal bases for estimation.

Simultaneous information and power transfer under a non-linear RF energy harvesting model

In the design of simultaneous wireless information and power transfer (SWIPT) systems, it has been typically assumed that energy conversion efficiency is independent from the level of the input power at the energy receiver. On the other hand, in practice the energy conversion efficiency exhibits a nonlinear behavior and highly depends on the input power. This leads to a discrepancy between the practical energy harvesting (EH) hardware available and the resource allocation designs made for the SWIPT systems. This work is concerned with this issue. In particular, we propose a practical quadratic model for the power conversion efficiency in EH circuitry. Comparisons with the constant efficiency models used in conventional SWIPT system design as well as another non-linear model proposed in the literature are made. With its convexity properties together with the good match it provides for the measurement data from practical EH circuitry, the proposed model is shown to be a promising alternative to the existing EH approaches. Using the proposed model, the problem of resource allocation for a multiuser Orthogonal Frequency-Division Multiple Access (OFDMA) system is investigated. The performance improvement due to the usage of the proposed non-linear model is illustrated.

Representation of optical fields using finite numbers of bits

We consider the problem of representation of a finite-energy optical field, with a finite number of bits. The optical field is represented with a finite number of uniformly spaced finite-accuracy samples (there is a finite number of amplitude levels that can be reliably distinguished for each sample). The total number of bits required to encode all samples constitutes the cost of the representation. We investigate the optimal number and spacing of these samples under a total cost budget. Our framework reveals the trade-off between the number, spacing, and accuracy of the samples. When we vary the cost budget, we obtain trade-off curves between the representation error and the cost budget. We also discuss the effect of degree of coherence of the field.

Optimal Measurement under Cost Constraints for Estimation of Propagating Wave Fields

We give a precise mathematical formulation of some measurement problems arising in optics, which is also applicable in a wide variety of other contexts. In essence the measurement problem is an estimation problem in which data collected by a number of noisy measurement probes are combined to reconstruct an unknown realization of a random process f(x) indexed by a spatial variable x epsi Rk for some k ges 1. We wish to optimally choose and position the probes given the statistical characterization of the process f(x) and of the measurement noise processes. We use a model in which we define a cost function for measurement probes depending on their resolving power. The estimation problem is then set up as an optimization problem in which we wish to minimize the mean-square estimation error summed over the entire domain of f subject to a total cost constraint for the probes. The decision variables are the number of probes, their positions and qualities. We are unable to offer a solution to this problem in such generality; however, for the metrical problem in which the number and locations of the probes are fixed, we give complete solutions for some special cases and an efficient numerical algorithm for computing the best trade-off between measurement cost and mean-square estimation error. A novel aspect of our formulation is its close connection with information theory; as we argue in the paper, the mutual information function is the natural cost function for a measurement device. The use of information as a cost measure for noisy measurements opens up several direct analogies between the measurement problem and classical problems of information theory, which are pointed out in the paper.

Beyond Nyquist sampling: A cost-based approach

A sampling-based framework for finding the optimal representation of a finite energy optical field using a finite number of bits is presented. For a given bit budget, we determine the optimum number and spacing of the samples in order to represent the field with as low error as possible. We present the associated performance bounds as trade-off curves between the error and the cost budget. In contrast to common practice, which often treats sampling and quantization separately, we explicitly focus on the interplay between limited spatial resolution and limited amplitude accuracy, such as whether it is better to take more samples with lower amplitude accuracy or fewer samples with higher accuracy. We illustrate that in certain cases sampling at rates different from the Nyquist rate is more efficient.

Performance bounds for remote estimation with an energy harvesting sensor

Remote estimation with an energy harvesting sensor with a limited data buffer is considered. The sensor node observes an unknown correlated circularly wide-sense stationary (c.w.s.s.) Gaussian field and communicates its observations to a remote fusion center using the energy it harvested. The fusion center employs minimum mean-square error (MMSE) estimation to reconstruct the unknown field. The distortion minimization problem under the online scheme, where the sensor has only access to the statistical information for the future energy packets is considered. We provide performance bounds on the achievable distortion under a slotted block transmission scheme, where at each transmission time slot, the data and the energy buffer is completely emptied. Our bounds provide insight to the trade-off between the buffer sizes and the achievable distortion. These trade-offs illustrate the insensitivity of the performance to the buffer size for signals with low degree of freedom and suggest performance improvements with increasing buffer size for signals with relatively higher degree of freedom.

Feasibility of Ambient RF Energy Harvesting for Self-Sustainable M2M Communications Using Transparent and Flexible Graphene Antennas

Lifetime is a critical parameter in ubiquitous, battery-operated sensors for machine-to-machine (M2M) communication systems, an emerging part of the future Internet of Things. In this paper, the performance of radio frequency (RF) to DC energy converters using transparent and flexible rectennas based on graphene in an ambient RF energy-harvesting scenario is evaluated. Full-wave electromagnetic (EM) simulations of a dipole antenna assuming the reported state-of-the-art sheet resistance for few-layer, transparent graphene yields an estimated ohmic efficiency of 5%. In the power budget calculation, the low efficiency of transparent graphene antennas is an issue because of the relatively low amount of available ambient RF energy in the frequency bands of interest, which together sets an upper limit on the harvested energy available for the RF-powered device. Using a commercial diode rectifier and an off-the-shelf wireless system for sensor communication, the graphene-based solution provides only a limited battery lifetime extension. However, for ultra-low-power technologies currently at the research stage, more advantageous ambient energy levels, or other use cases with infrequent data transmission, graphene-based solutions may be more feasible.

Cooperative Precoding and Artificial Noise Design for Security Over Interference Channels

We focus on linear precoding strategies as a physical layer technique for providing security in Gaussian interference channels. We consider an artificial noise aided scheme where transmitters may broadcast noise in addition to data in order to confuse eavesdroppers. We formulate the problem of minimizing the total mean-square error at the legitimate receivers while keeping the error values at the eavesdroppers above target levels. This set-up leads to a non-convex problem formulation. Hence, we propose a coordinate block descent technique based on a tight semi-definite relaxation and design linear precoders as well as spatial distribution of the artificial noise. Our results illustrate that artificial noise can provide significant performance gains especially when the secrecy levels required at the eavesdroppers are demanding.

Lower bounds on the error probability of turbo codes

We present lower bounds on the error probability of turbo codes under maximum likelihood (ML) decoding. We focus on additive white Gaussian noise (AWGN) channels, and consider both ensembles of codes with uniform interleaving and specific turbo codes with fixed interleavers. To calculate the lower bounds, instead of using the traditional approach that only makes use of the distance spectrum, we propose to utilize the exact second order distance spectrum. This approach together with a proper restriction of the error events results in promising lower bounds.