Keith A. Avery

Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas

This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover “white space” frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motors rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computers parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.

Asymmetric Cooperative Communications Based Spectrum Leasing via Auctions in Cognitive Radio Networks

Dynamic spectrum leasing (DSL) was proposed recently as a new paradigm for dynamic spectrum sharing (DSS) in cognitive radio networks (CRNs). In this paper, we propose a new way to encourage primary users to lease their spectrum: The secondary users (SUs) place bids indicating how much power they are willing to spend for relaying the primary signals to their destinations. In this formulation, the primary users achieve power savings due to asymmetric cooperation. We propose and analyze both a centralized and a distributed decision-making architecture for the secondary CRN. In the centralized architecture, a Secondary System Decision Center (SSDC) selects a bid for each primary channel based on optimal channel assignment for SUs. In the decentralized cognitive network architecture, we formulate an auction game-based protocol in which each SU independently places bids for each primary channel and receivers of each primary link pick the bid that will lead to the most power savings. A simple and robust distributed reinforcement learning mechanism is developed to allow the users to revise their bids and to increase their rewards. The performance results show the significant impact of reinforcement learning in both improving spectrum utilization and meeting individual SU performance requirements.

Demonstration of a Cognitive Radio Front End Using an Optically Pumped Reconfigurable Antenna System (OPRAS)

A cognitive radio front end using an optically pumped reconfigurable antenna system (OPRAS) is investigated. The scheme consists of a ultrawideband antenna and a reconfigurable narrowband antenna in close proximity to one another. The narrowband reconfigurability is achieved by a integrating laser diodes within the antenna structure to control the switching state of photoconductive silicon switches. This scheme has the advantage of eliminating the use of optical fiber cables to guide light to the switches, and enables easier integration of the reconfigurable antenna in a complete communication system. The performance of the proposed technique is presented, and comparisons are made to other commonly used switching techniques for reconfigurable antennas, such as techniques based on PIN diodes and RF microlectromechanical systems integration. The application of this antenna design scheme serving as the receive channel in a cognitive radio communication link is also demonstrated.

Wideband Spectrum Sensing and Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios

This paper presents an autonomous cognitive radio (CR) architecture, referred to as the Radiobot. This model goes beyond adaptive radio systems to exploit the main ingredients of cognition which, in this context, are mainly self-learning and self-reconfiguration. Without any prior knowledge of the RF environment, the Radiobot applies a sequence of increasingly sophisticated processing steps to detect and identify the sensed signals. In particular, in this paper, it applies a blind energy detection followed by a cyclostationary detection method to detect the active signals and extract their underlying periodic properties as reflected in cyclic frequencies. These extracted signal features are classified based on the Chinese restaurant process (CRP) and a learning algorithm is applied to achieve autonomous self-reconfiguration of the sensing module. We analyze the impact of fading and Doppler frequency shift on both the energy and cyclostationary detections, and show the receiver operating characteristic (ROC) of the carrier frequency detector. We show the robustness of the cyclostationary detection against channel fading and wide-sense stationary noise. Simulation results are presented to verify the multi-band operability and the reconfiguration ability of the Radiobot and to verify the convergence of the proposed learning algorithm.

Optimal Myopic Sensing and Dynamic Spectrum Access in Cognitive Radio Networks with Low-Complexity Implementations

Cognitive radio techniques allow secondary users (SUs) to opportunistically access underutilized primary channels that are licensed to primary users. We consider a group of SUs with limited spectrum sensing capabilities working cooperatively to find primary channel spectrum holes. The objective is to design the optimal sensing and access policies that maximize the total secondary throughput on primary channels accrued over time. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are intractable. Instead, we find the optimal sensing policy within the class of myopic policies. Compared to other existing approaches, our policy is more realistic because it explicitly assigns SUs to sense specific primary channels by taking into account spatial and temporal variations of primary channels. Contributions: (1) formulation of a centralized spectrum sensing/access architecture that allows exploitation of all available primary spectrum holes; and (2) proposing sub-optimal myopic sensing policies with low-complexity implementations and performance close to the myopic policy. We show that our proposed sensing/access policy is close to the optimal POMDP solution and outperforms other proposed strategies. We also propose a Hidden Markov Model based algorithm to estimate the parameters of primary channel Markov models with a linear complexity.

Blind cyclostationary feature detection based spectrum sensing for autonomous self-learning cognitive radios

In this paper, we present an autonomous cognitive radio (CR) architecture that incorporates the main features of cognition. This model, referred to as the Radiobot, is capable of self-learning and self-reconfiguration to match its RF environment. The proposed CR architecture assumes a joint blind energy and cyclostationary detection methods to classify the communication systems in its vicinity, without any prior knowledge of the sensed signals. We derive the receiver operating characteristic (ROC) of the energy detector and show, analytically, the impact of the sliding window length on the energy detection. A learning algorithm is proposed, allowing the Radiobot to independently learn from its past experience in order to optimize its operating parameters. By applying the learning algorithm to the sensing module, we verify, through simulations, the convergence of the proposed algorithm to the optimal solution.

Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels

In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.

Total Dose Test Results for CubeSat Electronics

CubeSats are increasingly important for space research. Their low orbits and short mission durations permit using electronics with modest radiation failure thresholds. Total ionizing dose irradiation results are presented for microelectronics interesting for CubeSat applications.

Radiobots: The autonomous, self-learning future cognitive radios

A futuristic autonomous self-learning cognitive radio (CR), also called as a Radiobot, is proposed. The Radiobot is defined to be a radio device that is capable of self-managing and self-reconfiguring in real-time to match its RF environment while continuously self-learning from its past experience to achieve: 1) autonomous communication and awareness of experienced RF environment, 2) spectrum coexistence/efficiency including dynamic spectrum sharing (DSS), 3) inter-operability in heterogeneous RF network environments, 4) multi-mode operability (simultaneous operation over multiple modes/networks), and 5) power efficient green communications. In this paper, we present a system level architecture of the Radiobot and our current work on self-learning guided wide-band spectrum-sensing. We also discuss future research directions in order to make the concept a reality, including necessary cognitive algorithms critical for its operation and the need for real-time reconfigurable hardware and RF antennas.

Total Ionizing Dose Effects on Commercial Electronics for Cube Sats in Low Earth Orbits

Modest total dose in low earth orbit and short cube sat missions provide an opportunity for using commercial electronics. We present the results of high and low dose rate testing of candidate commercial microcircuits.