Tamal Bose

A nullspace-based precoder with subspace expansion for radar/communications coexistence

In this paper we consider spectrum sharing between a communication system, modeled as MIMO interference channel (MIMO IFC), and MIMO radar. We first derive a zero-forcing precoder to eliminate the interference at the communication receivers. We show that this precoder is equivalent to orthogonal projection matrix onto the null space of an effective interference channel and show that this choice of precoder will degrade the radar target direction estimation performance. We show that the radar performance can be improved at the cost of non-zero interference at communication users by projecting the radar signal onto a subspace which is an expanded version of the aforementioned null space. We propose two approaches to smoothly expand the projected subspace and study the tradeoff between radar performance and interference at the communication users through simulation.

Metacognitive Radio Engine Design and Standardization

In this paper, we provide an overview of how cognitive radio (CR) technology is making its way into the current wireless standards, and we discuss further opportunities for more CR-oriented standardization efforts. Specifically, we discuss how a cognitive engine (CE) can be potentially standardized, and we offer our latest innovations on metacognitive engine (meta-CE) design. The intelligent techniques of a CR are implemented in an agent commonly referred to as a CE. A CE manages the CRs operation. Typically, a CE is based on a primary learning and optimization algorithm that is utilized within the CE. Each of these algorithms has its own strengths and deficiencies depending on the operating scenario that makes some of them more appropriate for specific operating conditions. A meta-CE is an engine that learns which CE is more appropriate to provide the adaptation needed for the operating conditions. To make a meta-CE possible, first, we need a method for characterizing the CE algorithms performance. Second, we should classify the operating conditions based on the CE algorithms characteristics in order to determine the most appropriate operating condition. Our results show that, when we use meta-CE techniques, we have about 30% increase in performance compared with using the best individual CE.

On multi-agent self-tuning consensus

This paper considers the consensus problem in complex networks of uncertain discrete time agents. The coupling parameters among agents are locally self tuned by least-mean square (LMS) algorithm, without using any global information. In this process each agent minimizes a local cost function dependent on the error between the agent state and the average of neighbors states. Provided that the network graph is strongly connected, it is shown that for each agent the sequence of coupling parameters is convergent, and all agent states converge toward the same constant value. It is demonstrated that in the face of unknown high-frequency gain, the proposed algorithms generate such coupling parameters so that the overall multi-agent system is marginally stable with only one pole on the unit circle, located at λ = 1 .

Cooperative Modulation Classification of multiple signals in Cognitive Radio Networks

Automatic Modulation Classification (AMC) is an important component in Cognitive Radio (CR) Networks. Multiuser AMC classifies the modulation schemes of simultaneous multiple unknown transmitters. In addition, cooperation among multiple CR receivers for modulation classification offers significant improvements in classification performance and overcomes the detrimental channel effects that degrades the single CR classifier performance. In this paper, a novel centralized soft-combining data fusion algorithm based on the joint probability distribution of fourth order cumulants is presented for cooperative modulation classification. Fourth order cumulants of the received signals are calculated as discriminating features for different modulation schemes at each CR node and sent to a centralized data Fusion Center (FC). The FC chooses the modulation scheme that maximizes the joint probability of the estimated cumulants. As compared to independent receiver classification, cooperative classification results are significantly improved under the same multi-path environment.

Metacognition and the next generation of cognitive radio engines

Much of the previous research on cognitive radio has focused on developing algorithms based on artificial neural networks, the genetic algorithm, and reinforcement learning, each with its pros and cons. In this research, we present a new approach based on metacognition. We believe that the metacognitive framework can be the foundation for the next generation of CRs and further the performance improvements in CR. In this work, we present the elements involved in metacognitive radio, discuss the challenges in their development, present solutions to the challenges along with a possible meta-CR architecture, and show results from our implementation. Each cognitive engine (CE) algorithm has strengths and limitations that make it more suitable for certain operating scenarios (channel conditions, operating objective, available hardware, etc.) than other algorithms. A meta-CE can adapt faster and improve performance by exploiting the characteristics and expected performance of the individual CE algorithms. It understands the operational scenarios and utilizes the most appropriate algorithm for the current operational scenario by switching between the algorithms or adjusting them as necessary.

Remote Transmitter Tracking with Raytraced Fingerprint Database

This paper investigates a method of tracking remote transmitters using a fingerprint localization database generated by ray tracing. OpenStreetMap building data is used to model the area of interest, and a database of received signal strength (RSS), time-difference of arrival (TDOA), and angle of arrival (AOA) fingerprints is generated virtually along a grid. These fingerprints are then used to estimate the origin of incoming signals. Simulation of this method using three receivers and a 10m square grid demonstrates a localization accuracy within 15m.

Cooperative cumulants-based Modulation Classification under flat Rayleigh fading channels

Automatic Modulation Classification is a key technology in Cognitive Radio Networks. Blind identification of the modulation scheme of an unknown detected signal has various commercial and military applications. Performance of Automatic Modulation Classifiers degrades severely under low Signal-to-Noise ratios and fading channel scenarios. Cooperative classification is presented as a means to enhance the classification performance as well as to relax the computational constraints on individual nodes. In this work, the performance of cooperative cumulants-based modulation classification is studied under flat Rayleigh fading channels. The degradation in performance of a single node under flat Rayleigh fading is first presented in comparison to Additive White Gaussian Noise channels. Next, performance improvement obtained through cooperative combining of classification data from several nodes is presented. Analytical results as well as simulations show that cooperation will improve the overall performance of modulation classifiers, overcoming the performance loss due to fading and reaching classification results comparable to the AWGN scenario.

An initial approach towards quality of service based Spectrum Trading

Spectrum scarcity has become an important issue as demands for higher data rates increase in diverse wireless applications and aerospace communication scenarios. To address this problem, it becomes necessary to manage radio spectrum assignment in a way that optimizes the distribution of spectrum resources among several users while taking into account the quality of service (QoS) characteristics desired by the users of spectrum. In this paper, a novel approach to managing spectrum assignment based on Spectrum Trading (ST) will be presented. Market based spectrum assignment mechanisms such as spectrum trading are of growing interest to many spectrum management agencies that are planning to increase the use of these mechanisms for spectrum management and reduce their emphasis on command and control methods. This paper presents some of our initial work into incorporating quality of service information into the mechanisms that determine how spectrum should be traded when using a spectrum exchange. Through simulations and a testbed implementation of a QoS aware spectrum exchange our results show the viability of using QoS based mechanisms in spectrum trading and in the enhancement of dynamic spectrum assignment systems.

On the performance of SC-MMSE-FD equalization for fixed-point implementations

A fixed-point implementation of a minimum mean square error (MMSE) based frequency domain (FD) equalizer with soft interference cancellation (SC) is studied. The equalizer additionally processes a priori information about the transmitted symbols and is used for turbo equalization. In this paper, we analyze the quantization and the clipping for different fixed-point representations and modulation schemes. The analysis allows to derive efficient representations for all symbols within the equalizer. This procedure is demonstrated for a generic system configuration featuring a 16-QAM. Finally, a fixed-point implementation in an integrated design environment for FPGAs verifies the theoretical studies and shows the device utilizations for different FPGAs that are embedded in current software defined radios. The results show, that on average 10 bits per symbol are required for a near-optimum equalization performance utilizing less than 8% area of state of the art FPGAs.

A Spatial Interpolation Method for Radio Frequency Maps Based on the Discrete Cosine Transform

Estimating radio frequency (RF) power in a geographic area is almost as old as wireless technology itself. It is crucial to anyone who is working or doing business with wireless technologies: wireless operators, the military, regulators, etc. Under a cognitive radio paradigm, spectrum usage is no longer static and is migrating towards dynamic and shared access paradigms. Enabling this evolution requires the estimation of the Radio Frequency map (RF map) of a given area. A variety of methods have already been proposed and tested to accomplish this estimation by leveraging decades of work on spatial interpolation methods. In this work, we justify, through analysis and simulation, using the Discrete Cosine Transform (DCT) as a more dynamic and accurate interpolation technique through comparison to other deterministic methods such as the Inverse Distance Weighting (IDW) method.