Online Learning for Spectrum Sensing and Reconfigurable Antenna Control

Abstract

Efficient dynamic spectrum access (DSA) policies rely on accurate spectrum sensing information to exploit spectrum white space optimally. Obtaining accurate channel state information (CSI) from local spectrum measurements is made difficult by wireless signal fading and the presence of thermal noise which distorts measured signals and leads to uncertainty regarding the occupancy of spectrum resources. Electrically reconfigurable antenna systems (ERAS) offer the system designer an additional degree of freedom to exploit pattern and polarization diversity to improve the accuracy of local spectrum sensing decisions. We propose a learning technique to exploit pattern and polarization diversity offered by ERAS to improve spectrum sensing accuracy. While the proposed approach is designed to work within the unique design constraints of reconfigurable antennas, the approach is not antenna specific and will work with a wide variety of reconfigurable antenna designs. Validation of system performance is provided from measurements taken using the wireless open access research platform (WARP) software defined radio (SDR) platform in an indoor office environment.

ELASTIC- Enabling Massive-Antenna for Joint Spectrum Sensing and Sharing: How Many Antennas Do We Need?

“…• Sensing multi-channels simultaneously [32], [33]: Within the framework of ELASTIC, a new approach is considered: the antennas are clustered so that each set is used to sense certain channel and/or certain direction. Hence, several spectrum holes could be found at the same time.…”

“…where µ λ1 , µ Tn and σ 2 λ1 , σ 2 Tn are, respectively, the mean and the variance of λ 1 given by (19), (31) and (20), and T n given by (32). The parameter c is given by c = σ λ1 σ Tn γ where γ is the correlation coefficient between λ 1 and T n provided by (33).…”

QoS Driven Channel Selection Algorithm for Cognitive Radio Network: Multi-User Multi-Armed Bandit Approach

“…The proposed algorithms in this paper is able to take into account quality metric related to soft output of any spectrum sensing detector [22]. There have been some attempts in [23], [24], to consider the energy detector soft output as a reward for general reinforcement learning algorithms, but they lack from significant theoretical guarantee and a relation with achievable throughput.…”

“…This can be justified by the fact that ln t grows always slower than t and so w 1 which can be viewed as a fraction of t. Using inequalities in (24) and after replacing C ( w 1 , t ) and D 1 ( w 1 , t ) by their values and after some calculus we get…”

Compact Reconfigurable Antenna with an Omnidirectional Pattern and Four Directional Patterns for Wireless Sensor Systems

“…Recently, configurable antennas, as a part of smart wireless sensor networks (WSNs), have been extensively researched [ 7 , 8 , 9 ] and widely used in mobile devices [ 10 ], cancer detection sensors [ 11 ], self-healing sensors [ 12 ], and wearable sensors [ 13 , 14 ]. The configurable antennas used in smart sensor systems can be divided into two kinds, frequency-reconfigurable antennas (FRA) [ 15 , 16 , 17 , 18 , 19 ] and pattern-reconfigurable antennas (PRA) [ 20 , 21 , 22 , 23 , 24 ]. FRA can be applied to telemetry [ 16 ], motion detection [ 17 ], temperature monitoring [ 18 , 19 ], and so on.…”