Sensing or Transmission: Causal Cognitive Radio Strategies with Censorship

This paper introduces a novel opportunistic transmission strategy for cognitive radios (CRs). The primary user (PU) is assumed to transmit in a time-slotted manner according to a two-state Markov model, and the CR is either sensing, that is, obtaining a causal, noisy observation of a primary user (PU) state, or transmitting, but not both at the same time. In other words, the CR observations of the PU are censored whenever the CR is transmitting. The objective of the CR transmission strategy is to maximize the utilization ratio (UR), i.e., the relative number of the PU-idle slots that are used by the CR, subject to that the interference ratio (IR), i.e., the relative number of the PU-active slots that are used by the CR, is below a certain level. We introduce an a-posteriori LLR-based CR transmission strategy, called CLAPP, and evaluate this strategy in terms of the achievable UR for different PU model parameters and received signal-to-noise ratios (SNRs). The performance of CLAPP is compared with a simple censored energy detection scheme. Simulation results show that CLAPP has 52% gain in UR over the best censored energy detection scheme for a maximum IR level of 10% and an SNR of -2dB. \ua9 2002-2012 IEEE

TOWARD ENHANCED WIRELESS COEXISTENCE IN THE 2.4GHZ ISM BAND VIA TEMPORAL CHARACTERIZATION AND EMPIRICAL MODELING OF 802.11B/G/N NETWORKS A DISSERTATION

This dissertation presents an extensive experimental characterization and empirical modelling of 802.11 temporal behavior. A detailed characterization of 802.11b/g/n homogeneous and heterogeneous network traffic patterns is featured, including idle time distribution and channel utilization. Duty cycle serves as a measure for spectrum busyness. Higher duty cycle levels directly impact transceivers using the spectrum, which either refrain from transmission or suffer from increased errors. Duty cycle results are provided for 802.11b, g and n Wi-Fi technologies at various throughput levels. Lower values are observed for 802.11b and g networks. Spectrum occupancy measurements are essential for wireless networks planning and deployment. Detailed characterization of 802.11g/n homogeneous and heterogeneous network traffic patterns, including activity and idle time distribution are presented. Distributions were obtained from time domain measurements and represent time fragment distributions for active and inactive periods during a specific test. This information can assist other wireless technologies in using the crowded ISM band more efficiently and achieve enhanced wireless coexistence. Empirical models of 802.11 networks in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band are also presented. This information can assist other wireless technologies aiming to utilize the crowded ISM band more efficiently and achieve enhanced wireless coexistence. In this work models are derived for both homogeneous and heterogeneous 802.11 network idle time distribution. Additionally, two applications of 802.11 networks temporal characterization are presented. The first application investigates a novel method for identifying wireless technologies through the use of simple energy detection techniques to measure the channel temporal characteristics including activity and idle time probability distributions. In this work, a wireless technology identification algorithm was assessed experimentally. Temporal traffic pattern for 802.11b/g/n homogeneous and heterogeneous networks were measured and used as algorithm input. Identification accuracies of up to 96.83% and 85.9% are achieved for homogeneous and heterogeneous networks, respectively. The second application provides a case study using 802.15.4 ZigBee transmitter packet size on-line adjustments is also presented. Packet size is adaptively modified based on channel idle time distribution obtained using simple channel power measurements. Results demonstrate improved ZigBee performance and significant enhancement in throughput as a result of using adaptive packet size transmissions

Spectrum sensing optimisation for dynamic primary user signal

We propose a multi-layer spectrum sensing optimisation algorithm to maximise sensing efficiency by computing the optimal sensing and transmission durations for a fast changing, dynamic primary user. Dynamic primary user traffic is modelled as a random process, where the primary user changes states during both the sensing period and transmission period to reflect a more realistic scenario. Furthermore, we formulate joint constraints to correctly reflect interference to the primary user and lost opportunity of the secondary user during the transmission period. Finally, we implement a novel duty cycle based detector that is optimised with respect to PU traffic to accurately detect primary user activity during the sensing period. Simulation results show that unlike currently used detection models, the proposed algorithm can jointly optimise the sensing and transmission durations to simultaneously satisfy the optimisation constraints for the considered primary user traffic