Nicholas J. Kirsch

Design and Evaluation of a Reconfigurable Antenna Array for MIMO Systems

New reconfigurable antenna array is demonstrated for multiple input multiple output (MIMO) communication systems that improves link capacity in closely spaced antenna arrays. The antenna system consists of an array of two printed dipoles separated by a distance of a quarter wavelength. Each of the dipoles can be reconfigured in length using PIN diode switches. The switch configuration can be modified in a manner adaptive to changes in the environment. The configuration of switches effects the mutual coupling between the array elements, and subsequently, the radiation pattern of each antenna, leading to different degrees of pattern diversity which can be used to improve link capacity. The PIN diode-based reconfigurable antenna solution is first motivated through a capacity analysis of the antenna in a clustered MIMO channel model. A new definition of spatial correlation coefficient is introduced to include the effects of antenna mismatch and radiation efficiency when quantifying the benefit of pattern diversity. Next, the widespread applicability of the proposed technique is demonstrated, relative to conventional half wavelength printed dipoles, using computational electromagnetic simulation in an outdoor and indoor environment and field measurements in an indoor laboratory environment. It is shown for the 2 times 2 system considered in this paper, that an average improvement of 10% and 8% is achieved in link capacity for a signal to noise ratio (SNR) respectively of 10 dB and 20 dB in an indoor environment compared to a system employing non reconfigurable antenna arrays.

Low-threshold-current-density 1300-nm dilute-nitride quantum well lasers

Metalorganic chemical vapor deposition-grown In0.4Ga0.6As0.995N0.005 quantum well (QW) lasers have been realized, at an emission wavelength of 1.295 μm, with threshold and transparency current densities as low as 211 A/cm2 (for L=2000 μm) and 75 A/cm2, respectively. The utilization of a tensile-strained GaAs0.67P0.33 buffer layer and GaAs0.85P0.15 barrier layers allows a highly-compressively-strained In0.4Ga0.6As0.995N0.005 QW to be grown on a high-Al-content lower cladding layer, resulting in devices with high current injection efficiency (ηinj∼97%).

Optically transparent conductive polymer RFID meandering dipole antenna

In this paper, we present optically transparent flexible conductive polymer antennas for radio frequency identification systems. The designs for these antennas are presented along with simulated and measured results of antenna radiating properties. These conductive polymer antennas are compared to antennas with the same design fabricated out of copper. Finally, we include an analysis of the optical transparency of the conductive polymer antennas.

Performance of transparent conductive polymer antennas in a MIMO ad-hoc network

Multiple antenna communication systems are a solution to meet the demand for pervasive computing and ubiquitous wireless communications. As multiple antenna communication systems become more broadly deployed, integrating unobtrusive antennas into various form factors is becoming increasingly important. Antennas that are flexible and transparent can ease this design constraint. In this paper, we present a dipole conductive polymer antenna that is flexible and transparent. The focus of this paper is to show how well this antenna works in a communications network by evaluating channel capacity and packet error rate.

Improving the performance of vehicular networks in high traffic density conditions with cognitive radios

Vehicular ad hoc networks offer safety and travel efficiency by sharing information between vehicles and roadside units. The performance of current proposed standard can suffer when there is a large amount of spectral congestion. Spectral congestion can result when there is high vehicle density such as traffic jams. In this paper, we propose a cognitive radio system to spatially and temporally add additional channels to VANETs. This additional spectrum can increase the throughput and decrease the probability of packet collisions.

A De-noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions

Empirical mode decomposition is a nonparametric adaptive tool that decomposes signals into a set of zero-mean modes called intrinsic mode functions (IMFs) that can be used to denoise a signal by selecting the relevant (noise-free) modes. In this paper, the statistical properties of IMFs, produced by a range of signal distributions, are examined. Insight from the statistical analysis is used in a null hypothesis test that validates a best fit model distribution of the IMFs. This test is then used in a de-noising scheme, which is evaluated using different test signals corrupted with white and colored noise. Simulations under different scenarios demonstrate the efficacy of the proposed method as compared to other EMD-based de-noising techniques.

An EMD-Based Spectrum Sensing Technique for Cognitive Radio Networks

Precise noise estimation is essential in enhancing spectrum sensing techniques as estimating the wrong noise power may result in increasing the missed detection or false alarm rates. In this paper, we have created a new technique based on Empirical Mode Decomposition (EMD). Intrinsic Mode Functions (EMD output) of Gaussian noise were proven to follow a distinct characteristic, or behavior. This behavior is exploited to make an occupancy decision for a given bandwidth. Further, a non- parametric threshold is derived and used to determine how many occupied channels are presented. The proposed method exhibits a probability of detection of 96% at -12 dB. Further, a gain of 11 dB is achieved when comparing this method with other EMD-based spectrum sensing approaches.

An EMD-Based Double Threshold Detector for Spectrum Sensing in Cognitive Radio Networks

In this paper, an adaptive multi-channel energy detector is presented for spectrum sensing applications. This method exploits the Empirical Mode Decomposition (EMD) and Cell-Averaging Constant False Alarm Rate (CA-CFAR) in an effort to maximize the probability of detection for a given probability of false alarm. First, the oversampled baseband signal is compared to its corresponding EMD noise-only model. If the band is occupied, then an EMD-CA technique is used to estimate the noise power for a given false alarm probability. Then, from the estimated noise power, double thresholds based on two given false alarm rates are calculated to detect and localize the occupied channels of band of interest. The proposed approach is able to work blindly and it is independent of the noise power. Simulations for different sampling and false alarm rates are used to validate the performance of the proposed detector. The results revealed the robustness of the proposed technique to the noise uncertainty and the capability to sense and localize multiple channels simultaneously.

Accelerated learning in machine learning-based resource allocation methods for Heterogenous Networks

Heterogeneous Networks, such as those with Femtocells and Macrocell Basestations, face the task of resource allocation to ensure all users, both primary (mobile user) and secondary (femtocell user), receive assurances of quality of service. One method of performing this allocation, Q-learning, involves the use of a reward function (defining objectives) and a Q-table (storing policy information). This Q-table can be shared between users to speed up convergence on a policy ensuring a desired quality of service. In this paper, a reward function and state structure are presented and compared to another Q-learning reward function. The designed RF is shown to increase the sum femtocell user capacity in most scenarios while maintaining the desired quality of service for the mobile user. The sharing of Q-tables formed using th e designed reward function and state structure with nodes entering the network is shown to significantly speed up convergence in most scenarios when compared to convergence without sharing Q-tables.

An Indoor Probabilistic Localization Method Using Prior Information

In this paper, we propose a new probabilistic method for determining the position of an unknown node in an indoor environment. Our analysis shows that using a small subset of sensors reduces the error in comparison to larger sets. The best subset of sensors is determined by matching the power received by all of the sensors and comparing it to prior measurements. We present experimental measurements made that show the efficacy of this approach and compare this method to previously published techniques. Our analysis shows that the new method, Prior Measurement Comparison (PMC), yields greater estimation accuracy resulting in lower error.