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| Abstracts of some of our recent projects (in an arbitrary order) can be found below: | Abstracts of some of our recent projects (in an arbitrary order) can be found below: | ||
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| + | > **Spectrum sensing for cognitive radio.** We considered the case where the primary transmitter’s signal occupies a large bandwidth, but with a high power narrowband component. Such a signal opens up two possibilities for detection: one could either try to detect the entire wideband signal, or one could filter out only the narrowband component and detect its presence or absence. The fact that the signal propagation characteristics of narrowband and wideband signals are quite different leads to an interesting tradeoff between the two options. We analytically characterized this tradeoff, and determined which of the two options offers better detection performance. | ||
| + | > Joint work with Sanjeev Gurugopinath. | ||
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| + | > **Achievable rates and outer bounds for the interference channel with cooperation and privacy constraints.** In this line of research, we first developed algorithms for interference alignment in a multiuser MIMO interference constant channel setup. Next, we analyzed the generalized degrees of freedom of the K-user MIMO interference channel. Finally, we considered the 2-user interference channel with rate-limited cooperation between the transmitters and secrecy constraints at the receivers. We were able to derive achievable schemes and outer bounds on the rate pairs first for the so-called deterministic approximation of the interference channel, and then extended the results to the Gaussian interference channel. | ||
| + | > Joint work with Parthajit Mohapatra. | ||
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| + | > **Algorithms for sparse signal recovery.** We developed algorithms for wideband channel estimation exploiting the sparsity of the channel in the time-domain. Starting from algorithms for channel estimation in the single-antenna case, we extended our algorithms to track time-variations in the channel, to exploit “cluster sparsity”, where the signals arrive in clusters of closely separated delays, and to multiple antenna systems where the signals between different pairs of transmit and receive antennas have a common sparsity structure (support). This latter work won the best paper award in the National Communications Conference 2014, which was held at IIT Kanpur. | ||
| + | > Joint work with Ranjitha Prasad. | ||
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| + | > **Finding healthy individuals from a large population.** Here, one considers the scenario where a large population contains a small number of “defective” items. A commonly encountered goal is that of identifying the defective items, for example, to isolate them from the population. In the classical non-adaptive group testing approach, one groups the items into subsets, or pools, and runs tests on each pool. Tests come out positive if the pool contains at least one defective item, and come out negative if all items in the pool are non-defective. Using the outcomes of the tests, a fundamental goal of group testing is to reliably identify the complete set of defective items with as few tests as possible. We studied a variant of this problem: one of non-defective subset identification, where the primary goal is to identify a "subset" of “non-defective” items given the test outcomes. This is useful when one wants to quickly identify a batch of non-defective items, for example, when it is required to send out a shipment of items on high priority. We have developed fundamental limits on the number of tests required for non-defective subset recovery, as well as computationally efficient algorithms for recovering the non-defective items that approach the bounds. | ||
| + | > Joint work with Abhay Sharma. | ||
| > **Performance Comparison of Energy, Matched-Filter and Cyclostationarity-Based Spectrum Sensing:** This work addresses //spectrum sensing//, i.e., the problem of detecting the presence or absence of a primary signal by a cognitive radio. Three popular choices are comprehensively contrasted: energy detection, matched-filter detection and cyclostationarity-based detection. For the cyclostationarity-based detection, two options for signal detection are investigated: using the Spectral Correlation Density (SCD) function, and using the Magnitude Squared Coherence (MSC) function. Analytical expressions for the probability of detection and false alarm performance of the cyclostationary feature detectors as well as the ED and MFD, as a function of the SNR and sensing duration are derived. It is shown that the cyclostationarity-based detectors are naturally insensitive to uncertainty in the noise variance, as the decision statistic is based on the noise rejection property of the cyclostationary spectrum. Monte-Carlo simulations of the receiver operating characteristics corroborate the theoretical results, and illustrate the significant performance improvement offered by the MSC relative to the ED in a noise uncertain environment. The MFD, on the other hand, serves as an important performance benchmark. | > **Performance Comparison of Energy, Matched-Filter and Cyclostationarity-Based Spectrum Sensing:** This work addresses //spectrum sensing//, i.e., the problem of detecting the presence or absence of a primary signal by a cognitive radio. Three popular choices are comprehensively contrasted: energy detection, matched-filter detection and cyclostationarity-based detection. For the cyclostationarity-based detection, two options for signal detection are investigated: using the Spectral Correlation Density (SCD) function, and using the Magnitude Squared Coherence (MSC) function. Analytical expressions for the probability of detection and false alarm performance of the cyclostationary feature detectors as well as the ED and MFD, as a function of the SNR and sensing duration are derived. It is shown that the cyclostationarity-based detectors are naturally insensitive to uncertainty in the noise variance, as the decision statistic is based on the noise rejection property of the cyclostationary spectrum. Monte-Carlo simulations of the receiver operating characteristics corroborate the theoretical results, and illustrate the significant performance improvement offered by the MSC relative to the ED in a noise uncertain environment. The MFD, on the other hand, serves as an important performance benchmark. | ||
