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Maximizing available spectrum for cognitive radios

  • Author(s): Mishra, Shridhar Mubaraq
  • Advisor(s): Brodersen, Robert W
  • Sahai, Anant
  • et al.
Abstract

Cognitive radios have been proposed to address the dual problem of spectrum under-utilization and the need for vast swathes of new spectrum in the 0-3GHz band for wireless data services. Previous research in the area of cognitive radios has concentrated on signal processing (SP) innovations for improved detection with a limited number of samples (a.k.a detection sensitivity). However the SP perspective alone is unable to recover much unused spectrum. In this thesis we focus on two fronts to maximize the recovery of unused spectrum. First, we take a spatial perspective on spectrum usage. Second, we will look at advanced algorithms which use other radios and frequencies to aid the detection process.

The spatial perspective is necessitated by the FCC's decision to open up TV bands for ‘white space’/opportunistic spectrum use. Examination of the FCC's choice of parameters reveals the political and engineering tradeoffs made by the FCC. The FCC's rules applied to the database of TV transmitters and the population density of the United States as per the census of 2000 reveal that the geo-location rules enable an average of 9 white space channels per person. This number does not change significantly even if the secondaries take a purely selfish approach and operate only where the pollution from TV transmitters is low. The corresponding political tradeoff sacrifices 1 people-channel for broadcast use to gain 8 people-channels for white space use. Spectrum sensing is another mechanism to recover the available opportunity. Here, the FCC's sensing rule of -114dBm is very conservative and yields only a average of 1 white space channel per person.

This spatial analysis points to the inability of traditional detection metrics in predicting the spatial performance of sensing. To overcome this problem, we propose the twin metrics of Fear of Harmful Interference (FHI) and Weighted Probability of Area Recovered (WPAR) to quantify the performance of sensing relative to geo-location. Fear of Harmful interference (FHI), captures the safety to the primary users and is largely the fading-aware probability of missed detection with modifications to allow easier incorporation of system-level uncertainty. Weighted Probability of Area Recovered (WPAR), captures the performance of spectrum sensing by appropriately weighting the probability of false alarm (PFA) across different spatial locations. These metrics give a unifying framework in which to compare different spectrum-sensing algorithms. Reasonable parameters for this metric (most crucially, something which can be interpreted as a spatial discount factor) are obtained from the TV database and US population data.

Cooperative Sensing, in which secondaries use sensing results from multiple nearby radios to decide on whether the band is free to use, has been proposed as a mechanism to improve the performance over a single radio. However, the motivation for using cooperative sensing is different for recovering a temporal as opposed to recovering a spatial hole. For a temporal hole, cooperative sensing is used to gather as much energy in the primary signal as possible, thus maximizing the gap between the means of the signal present and signal absent hypotheses. For a spatial hole there is a mapping between the nominal signal strength at a given distance from the transmitter and the distance itself. By cooperating the hope is to converge to this nominal signal level. This difference has major implications on the choice of cooperation rule for different scenarios. A median rule is proposed as a robust cooperation rule for spatial holes. This rule can be implemented as a hard combining rule and is robust to uncertainties in the fading models and untrusted radio behavior.

Unfortunately all cooperation suffers when there is a loss in spatial diversity. To combat shadowing correlation across space, assisted/calibrated detection in the form of multiband sensing is proposed. In multiband sensing, detection results from nearby frequencies is used to aid detection in the frequency band of interest. Cooperation across multiband radios is robust against channel correlation and provides gains by weeding out radios afflicted by adverse propagation environments. A mobile, wideband testbed was designed and used to capture TV signals in the 500-700MHz band at various locations in Berkeley, CA. Analysis of these measurements reveals high shadowing and multipath spread correlation across frequencies. These new insights into the spectral environment are used to design detectors that perform better than a single band detector.

The ability to coexist with primaries of different scales (high power TV transmitters and low power Wireless Microphones) is an important requirement for a cognitive radio system. When sensing a large scale primary, a small scale secondary user can make its own decision about transmission based on the sensing results from its neighborhood. This assumption fails when the scale of the primary is comparable to the scale of the secondary user. In this scenario, we need to decouple sensing from admission control -- a sensor network is required to perform the sensing and localize the primary. For small primaries, the environment over which the sensing results are valid is small which imposes certain minimum density requirements for sensor nodes. Location information of the primary and secondary users is key for such an admission control algorithm to operate successfully.

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