Radio Environment Mapping and Sensing

WP3 advances against its set objectives and summary of technical highlights is as follows:

Modelling of Radio Environment

The aim of this task is to perform measurements about spectral activity, model the activity and finally emulate the radio scene. This has lead to the development of the original integrated so-called MME framework, standing for Measurements, Modeling and Emulation. The framework has been developed and proposed in a paper submitted to IEEE Vehicular Technology Magazine [1].

: Figure 1: MME approach

Such integrated approach guarantees to the user a high-fi mimicking of the radio scene which is crucial for reliable system assessment. Different flows targeting different goals can be envisaged. See [1] for further details. Several measurement campaigns have been performed so-far, over different bands and locations, brief summary is as follows:

ISM band measurements (power-based) by Oulu University
Wideband measurements (power based) with main focus on GSM by Aveiro University
GSM measurements (with decoding) by Thales in Paris
TV White Space measurements by UniS in Guildford, UK

New measurements are scheduled in 2012: correlated measurements by Aveiro University, CDMA measurements by Thales. From these measurements, modelling activities have been undertaken.

: Figure2: Exemplary measurement results [GSM band, Aveiro]

Framework for Radio Context Acquisition

Within this task we want to establish a framework for implementing sensing functionalities and a protocol stack for exchanging sensing information.

Based on the system reference model as defined in WP2 of QoSMOS, a specific model for the sensing is extracted. It incorporates the basic functionalities and builds a framework for all the activities related to sensing within QoSMOS. The model also highlights the different interfaces needed to communicate with other system blocks and with other sensing blocks.

Figure3 shows the latest version of this model. It consists of two main blocks, the SPECTRUM SENSING SENSOR CONTROL (SSS-CTRL) and the SPECTRUM SENSING MANAGEMENT (SS-MGT) block. Within these two blocks other blocks are incorporated which perform the sensing measurements and decisions and which use data-bases for having access to different models or algorithms. Also the interfaces to external entities are defined in the model.

This model is one of main outcomes of this task. With this, we have a working assumption for the future work and this model may be also subject to changes in the course of this task.

: Figure3: Reference model for sensing

The framework for sensing as defined in the reference model for sensing will be the basis for individual block design. The building blocks will be further developed by the partners involved in this part of QoSMOS. The partners are providing different kinds of sensing blocks and functionalities. Actually, the addressed blocks are Interference Monitoring, Detection Framework, Resource Reservation and a Sensing Quality Function. The requirements of the interfaces to the transceiver, to other sensing entities and to the spectrum and resource manager blocks are identified. These specifications are based on the analysis of the information flow between the blocks.

Algorithms for Radio Context Acquisition

Initial evaluation of algorithms for radio context acquisition has been completed. This ranges from classical radio context acquisition (i.e. local sensing) to disseminated sensing with exchange of collaboratively or cooperatively collected context data.

A plethora of local sensing algorithms is proposed in the literature. They are classically sorted in three main categories: energy detection, matched filtering and cyclostationary feature detection. New approaches, based on these three categories are investigated in this deliverable as well as their usage context:

Hybrid detection: It consists in mixing energy detection and cyclostationary feature detection. It will be proved to outperform the performance of both techniques taken separately.
Improved energy detection with background process for noise estimation. The proposed solution operates at two different time scales: a slow time scale to determine in adjacent sub-bands the supposed slowly varying noise level, and a faster time scale to determine in the band of interest the presence of signal, using a reliable energy detection solution. In order to identify the free bands where the noise variance can be estimated, one can use several blind and semi-blind strategies based on the statistical properties of the received signal.
Antenna processing: Classical OFDM detection algorithms based on the waveform characteristics and its cyclostationary features are extended the multi-antenna case (when using an antenna-array). We will show that smart antenna techniques not only improves the detection sensitivity but also allows non-OFDM spatial rejection permitting an opportunistic user to perform sensing on incumbent network without managing quiet periods.
Improved energy detection: Based on speech characteristics, energy detection algorithm for PMSE can be improved.
Statistical test theory: For energy detection, in order to improve noise level estimation when it follows some distribution properties, statistical test theory can be useful. Two tests are proposed, Anderson-Darling and Kolmogorov-Smirnov.

In the case of the TV white space reuse, classical sensing (meaning the detection of the presence of a signal in a given frequency band) might not be sufficient. Indeed, to verify that the reuse of the TV channel will not cause harmful interference to the incumbent receiver, the opportunistic system has to estimate the Carrier to Interference Ratio (CIR) of the incumbent receiver to determine the allowable transmit power. In order to do so a reliable CIR estimation technique is presented.

Distributed sensing is often presented as an alternative to improve local sensing performance, in presence, for example, of shadowing (presence of an obstacle between the sensing device and the emitter to be sensed). The issue addressed here is no longer the detection performance but how to transmit the sensing metrics to the fusion centre and how to merge numerous/various sensing metrics in an optimal way. In this deliverable, several key aspects of distributed sensing are addressed:

The quantization of the metrics sent to the fusion centre: what is the metrics resolution to use in order not to degrade sensing performance and not to use too much resource for metrics transmission.
The latency of data transport.
The impact of the time variance caused by the mobility (key point of the QoSMOS project) of cognitive radios on the sensitivity of spectrum sensing algorithm. It is a new approach as most of the actual works suppose a stationary cognitive radio.

Following the FCC memorandum publication in September 2010, preliminary thoughts for sensing requirements are also presented.

References

[1] "MME approach for CR Systems Evaluation: Measurement, Modelling and Evaluation in Cognitive Radio Network”, Deepaknath Tandur, Jonathan Duplicy, Kamran Arshad, Klaus Moessner, David Depierre, Janne Lehtomäki, Keith Briggs, Luis Gonçalves and Atilio Gameiro, under review in IEEE Vehicular Technology Magazine;