Analysis of Linear Receivers in a Target SINR Game for Wireless Cognitive Networks

Abstract-Signal to Interference plus Noise Ratio (SINR) is a key parameter for every user in a wireless network. Different users with heterogeneous QoS requirements have different target SINR requirements. In cognitive radio (CR) networks, secondary users try to access the available spectrum in order to make successful transmissions. However, without proper regulation, they may transmit at their maximum power to achieve the highest possible SINR, which can be even worse than the current wasteful static spectrum utilization. A target SINR game (TSG) is a powerful tool to regulate each secondary user's behavior, provide them with decent SINRs (i.e. close to their target SINRs) and simultaneously limit the interference they cause to primary users and other secondary users. The goal of this paper is to analyze the performance of the Matched Filter (MF) receiver and the linear MMSE receiver (LMMSE) in a TSG. As expected, the LMMSE shows several advantages in performance over the MF

-***-****-* Information Theoretic Conditions for Tracking in Leader-Follower Systems with Communication Constraints

Abstract: In this paper, we introduce a general framework for tracking in leader-follower systems under communication constraints, in which the leader and follower systems as well as the corresponding controllers are spatially distributed and connected over communication links. We provide necessary conditions on the channel data rate of each communication link for tracking of the leader-follower systems. By considering the forward and feedback channels as one cascade channel, we also provide a lower bound for the data rate of the cascade channel for the system to track a reference signal such that the tracking error has finite second moment. Examples and simulations are provided to demonstrate some of the results

Cognitive Anti-Jamming Satellite-To-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on a live satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in-the- loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested on the NASA Space Communications and Navigation (SCaN) Testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine learning aided cognitive protocols against several different types of jammers

Cognitive Anti-jamming Satellite-to-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on an experimental satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in the-loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested with the NASA's Space Communications and Networking (SCaN) testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine-learning aided cognitive protocols against several different types of jammers

Signal processing for cognitive radios

This book covers power electronics, in depth, by presenting the basic principles and application details, and it can be used both as a textbook and reference book.  Introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios

Optimal node placement in decision fusion wireless sensor networks for distributed detection of a randomly-located target

ABSTRACT We consider the problem of optimal (fixed) wireless sensor network (WSN