Nikhil Gulati

Learning State Selection for Reconfigurable Antennas: A Multi-Armed Bandit Approach

Reconfigurable antennas are capable of dynamically re-shaping their radiation patterns in response to the needs of a wireless link or a network. In order to utilize the benefits of reconfigurable antennas, selecting an optimal antenna state for communication is essential and depends on the availability of full channel state information for all the available antenna states. We consider the problem of reconfigurable antenna state selection in a single user MIMO system. We first formulate the state selection as a multi-armed bandit problem that aims to optimize arbitrary link quality metrics. We then show that by using online learning under a multi-armed bandit framework, a sequential decision policy can be employed to learn optimal antenna states without instantaneous full CSI and without a priori knowledge of wireless channel statistics. Our objective is to devise an adaptive state selection technique when the channels corresponding to all the states are not directly observable and compare our results against the case of a known model or genie with full information. We evaluate the performance of the proposed antenna state selection technique by identifying key link quality metrics and using measured channels in a 2 × 2 MIMO OFDM system. We show that the proposed technique maximizes long term link performance with reduced channel training frequency.

Sectorized Antenna-based DoA Estimation and Localization: Advanced Algorithms and Measurements

Sectorized antennas are a promising class of antennas for enabling direction-of-arrival (DoA) estimation and successive transmitter localization. In contrast to antenna arrays, sectorized antennas do not require multiple transceiver branches and can be implemented using a single RF front-end only, thus reducing the overall size and cost of the devices. However, for good localization performance the underlying DoA estimator is of uttermost importance. In this paper, we therefore propose a novel high performance DoA estimator for sectorized antennas that does not require cooperation between the transmitter and the localizing network. The proposed DoA estimator is broadly applicable with different sectorized antenna types and signal waveforms, and has low computational complexity. Using computer simulations, we show that our algorithm approaches the respective Cramer-Rao lower bound for DoA estimation variance if the signal-to-noise ratio (SNR) is moderate to large and also outperforms the existing estimators. Moreover, we also derive analytical error models for the underlying DoA estimation principle considering both free space as well as multipath propagation scenarios. Furthermore, we also address the fusion of the individual DoA estimates into a location estimate using the Stansfield algorithm and study the corresponding localization performance in detail. Finally, we show how to implement the localization in practical systems and demonstrate the achievable performance using indoor RF measurements obtained with practical sectorized antenna units.

Learning algorithm for reconfigurable antenna state selection

In this paper, we propose an online learning algorithm for selecting the state of a reconfigurable antenna. We formulate the antenna state selection as a multiarmed bandit problem and present a selection technique, implemented for a 2 × 2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. We quantify the performance of our selection technique using a software defined radio testbed and present results for a wireless network in a typical indoor environment.

Impact of pattern reconfigurable antennas on Interference Alignment over measured channels

In recent years, it has been shown that concentrating interference and desired signal into separate spaces using Interference Alignment (IA), can achieve the outer bound of the degrees of freedom for the interference channel. However, the non-orthogonality of signal and interference space limits sum capacity performance. In this paper, we present the notion that by using reconfigurable antenna based pattern diversity, the optimal channel can be realized in order to maximize the distance between the two subspaces, thereby increasing sum capacity. We experimentally validate our claim and show the benefits of pattern reconfigurability using real world channels, measured in a MIMO-OFDM interference network. We quantify the results with two different reconfigurable antenna architectures. We show that an additional 47% gain in chordal distance and 45% gain in sum capacity were achieved by exploiting pattern diversity with IA. We further show that due to optimal channel selection, the performance of IA can also be improved in a low SNR regime.

GMM Based Semi-Supervised Learning for Channel-Based Authentication Scheme

Authentication schemes based on wireless physical layer channel information have gained significant attention in recent years. It has been shown in recent studies, that the channel based authentication can either cooperate with existing higher layer security protocols or provide some degree of security to networks without central authority such as sensor networks. We propose a Gaussian Mixture Model based semi-supervised learning technique to identify intruders in the network by building a probabilistic model of the wireless channel of the network users. We show that even without having a complete apriori knowledge of the statistics of intruders and users in the network, our technique can learn and update the model in an online fashion while maintaining high detection rate. We experimentally demonstrate our proposed technique leveraging pattern diversity and show using measured channels that miss detection rates as low as 0.1% for false alarm rate of 0.3% can be achieved.

Experimental evaluation of a reconfigurable antenna system for blind interference alignment

In recent years, several experimental studies have come out to validate the theoretical findings of interference alignment (IA), but only a handful of studies have focused on blind interference alignment. Unlike IA and other interference mitigation techniques, blind IA does not require channel state information at the transmitter (CSIT). The key insight is that the transmitter uses the knowledge of channel coherence intervals and receivers utilize reconfigurable antennas to create channel fluctuations exploited by the transmitter. In this work, we present a novel experimental evaluation of a reconfigurable antenna system for achieving blind IA. We present a blind IA technique based on reconfigurable antennas for a 2-user multiple-input single-output (MISO) broadcast channel implemented on a software defined radio platform where each of the receivers is equipped with a reconfigurable antenna. We further compare this blind IA implementation with traditional TDMA scheme for benchmarking purposes. We show that the achievable rates for blind IA can be realized in practice using measured channels under practical channel conditions. Additionally, the average error vector magnitude and bit error rate (BER) performances are evaluated.

Evaluation of an accelerator architecture for speckle reducing anisotropic diffusion

Increasing chip power density has brought application specific accelerator architectures to the forefront as an energy and area efficient solution. While GPGPU systems take advantage of specialized hardware to perform computationally intensive tasks faster than chip multiprocessor (CMP) systems, accelerators are hardware units that are designed to execute a specific application efficiently. Real-time ultrasound imaging applications require the removal of multiplicative noise while maintaining a steady frame-rate, and are good candidates to explore accelerator-based systems. In this paper, we propose and evaluate the architecture of an accelerator designed to improve performance of SRAD image enhancing algorithm. We compare the projected performance of the SRAD accelerator to software implementations on a multi-core CPU and a CPU+GPU system. The proposed architecture achieves higher throughput by eliminating redundant fetches from memory and by storing intermediate data locally. The speedup of the GPU is found to be 3.2× over the CPU, while the accelerator achieved a speedup of 24×. The area efficiency of the GPU and accelerator is up to 1.6× and 370× better than the CPU, respectively. In comparison with the CPU, we find that the energy consumed for operation on a single frame is found to be 1.5× lesser on the GPU and up to 580× lesser on the accelerator.

Experimental Results of Novel DoA Estimation Algorithms for Compact Reconfigurable Antennas

Reconfigurable antenna systems have gained much attention for potential use in the next generation wireless systems. However, conventional direction-of-arrival (DoA) estimation algorithms for antenna arrays cannot be used directly in reconfigurable antennas due to different design of the antennas. In this paper, we present an adjacent pattern power ratio (APPR) algorithm for two-port composite right/left-handed (CRLH) reconfigurable leaky-wave antennas (LWAs). Additionally, we compare the performances of the APPR algorithm and LWA-based MUSIC algorithms. We study how the computational complexity and the performance of the algorithms depend on number of selected radiation patterns. In addition, we evaluate the performance of the APPR and MUSIC algorithms with numerical simulations as well as with real world indoor measurements having both line-of-sight and non-line-of-sight components. Our performance evaluations show that the DoA estimates are in a considerably good agreement with the real DoAs, especially with the APPR algorithm. In summary, the APPR and MUSIC algorithms for DoA estimation along with the planar and compact LWA layout can be a valuable solution to enhance the performance of the wireless communication in the next generation systems.

DoA Estimation Using Compact CRLH Leaky-Wave Antennas: Novel Algorithms and Measured Performance

Traditional direction-of-arrival (DoA) estimation algorithms for multielement antenna arrays (AAs) are not directly applicable to reconfigurable antennas due to inherent design and operating differences between AAs and reconfigurable antennas. In this paper, we propose novel modifications to the existing DoA algorithms and show how these can be adapted for real-time DoA estimation using two-port composite right/ left-handed (CRLH) reconfigurable leaky-wave antennas (LWAs). First, we propose a single/two-port multiple signal classification (MUSIC) algorithm and derive the corresponding steering vector for reconfigurable LWAs. We also present a power pattern cross correlation algorithm that is based on finding the maximum correlation between the measured radiation patterns and the received powers. For all algorithms, we show how to simultaneously use both ports of the two-port LWA in order to improve the DoA estimation accuracy and, at the same time, reduce the scanning time for the arriving signals. Moreover, we formulate the Cramer–Rao bound for MUSIC-based DoA estimation with LWAs and present an extensive performance evaluation of MUSIC algorithm based on numerical simulations. In addition, these results are compared to DoA estimation with conventional AAs. Finally, we experimentally evaluate the performance of the proposed algorithms in an indoor multipath wireless environment with both line-of-sight (LoS) and non-LoS components. Our results demonstrate that DoA estimation of the received signal can be successfully performed using the two-port CRLH-LWA, even in the presence of severe multipath.