Browsing by Author "Han, Zhu"

Now showing 1 - 5 of 5
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    Collaborative Spectrum Sensing from Sparse Observations Using Matrix Completion
    (2009-12) Meng, Jia; Yin, Wotao; Li, Husheng; Houssian, Ekram; Han, Zhu
    In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%.
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    Compressive Sensing Based High Resolution Channel Estimation for OFDM System
    (2011-08) Meng, Jia (Jasmine); Yin, Wotao; Li, Yingying; Nguyen, Nam T.; Han, Zhu
    Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next generation wireless communication. Channel estimation is one of the key challenges in OFDM, since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. In this paper, we propose a system with an asymmetric DAC/ADC pair and formulate OFDM channel estimation as a compressive sensing problem. By skillfully designing pilots and taking advantages of the sparsity of the channel impulse response, the proposed system realizes high resolution channel estimation at a low cost. The pilot design, the use of a high-speed DAC and a regular-speed ADC, and the estimation algorithm tailored for channel estimation distinguish the proposed approach from the existing estimation approaches. We theoretically show that in the proposed system, an N-resolution channel can be faithfully obtained with an ADC speed at M=O(S^2 log(N/S)), where N is also the DAC speed and S is the channel impulse response sparsity. Since S is small and increasing the DAC speed to N>M is relatively cheap, we obtain a high-resolution channel at a low cost. We also present a novel estimator that is both faster and more accurate than the typical L1 minimization. In the numerical experiments, we simulated various numbers of multipaths and different SNRs and let the transmitter DAC run at 16 times the speed of the receiver ADC for estimating channels at the 16x resolution. While there is no similar approaches (for asymmetric DAC/ADC pairs) to compare with, we derive the Cramer-Rao lower bound.
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    Dynamic Compressive Spectrum Sensing for Cognitive Radio Networks
    (2011-01) Yin, Wotao; Wen, Zaiwen; Li, Shuyi; Meng, Jia (Jasmine); Han, Zhu
    In the recently proposed collaborative compressive sensing, the cognitive radios (CRs) sense the occupied spectrum channels by measuring linear combinations of channel powers, instead of sweeping a set of channels sequentially. The measurements are reported to the fusion center, where the occupied channels are recovered by compressive sensing algorithms. In this paper, we study a method of dynamic compressive sensing, which continuously measures channel powers and recovers the occupied channels in a dynamic environment. While standard compressive sensing algorithms must recover multiple occupied channels, a dynamic algorithm only needs to recover the recent change, which is either a newly occupied channel or a released one. On the other hand, the dynamic algorithm must recover the change just in time. Therefore, we propose a least-squared based algorithm, which is equivalent to l0 minimization. We demonstrate its fast speed and robustness to noise. Simulation results demonstrate effectiveness of the proposed scheme.
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    High Resolution OFDM Channel Estimation with Low Speed ADC using Compressive Sensing
    (2010-11) Meng, Jia (Jasmine); Li, Yingying; Nguyen, Nam; Yin, Wotao; Han, Zhu
    Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next generation wireless communication. Channel estimation is one of the key challenges in an OFDM system. In this paper, we formulate OFDM channel estimation as a compressive sensing problem, which takes advantage of the sparsity of the channel impulse response and reduces the number of probing measurements, which in turn reduces the ADC speed needed for channel estimation. Specifically, we propose sending out pilots with random phases in order to "spread out" the sparse taps in the impulse response over the uniformly downsampled measurements at the low speed receiver ADC, so that the impulse response can still be recovered by sparse optimization. This contribution leads to high resolution channel estimation with low speed ADCs, distinguishing this paper from the existing attempts of OFDM channel estimation. We also propose a novel estimator that performs better than the commonly used L1 minimization. Specifically, it significantly reduces estimation error by combing L1 minimization with iterative support detection and limited-support least-squares. While letting the receiver ADC run at a speed as low as 1/16 of the speed of the transmitter DAC, we simulated various numbers of multipaths and different measurement SNRs. The proposed system has channel estimation resolution as high as the system equipped with the high speed ADCs, and the proposed algorithm provides additional 6 dB gain for signal to noise ratio.
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    Oil Spill Sensor using Multispectral Infrared Imaging via L1 Minimization
    (2010-11) Li, Yingying; Shih, Wei-Chuan; Han, Zhu; Yin, Wotao
    Early detection of oil spill events is the key to environmental protection and disaster management. Current technology lacks the sensitivity and specificity in detecting the early onset of a small-scale oil spill event. Based on an infrared oil-water contrast model recently developed, we propose a novel nonscanning computational infrared sensor that has the potential to achieve unprecedented detection sensitivity. Such a system can be very low-cost and robust for automated outdoor operations, leading to massive offshore deployment. Taking advantage of the characteristic oil thickness multispectral signatures, we have streamlined an algorithm that incorporates 3D image reconstruction and classification in a single inversion step capitalizing on the benefits of L1 minimization.