To opportunistically use the licensed band, spectrum sensing has a vital role as the core component in cognitive radio systems. However, the accurate detection of the primary signal is always accompanied by significant overhead, reducing the secondary throughput. In this letter, we suggest remedying this problem by adopting multiple-stage spectrum sensing (MSS) technique. Furthermore, we investigate how our proposed MSS can be incorporated into the collaborative spectrum sensing. Our results are encouraging in that the proposed MSS with collaboration significantly reduces the sensing time compared to the conventional sensing scheme.

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We propose a mobility enhancement method in which APs periodically change their RF channels in a predetermined order that prevents overlap of neighboring APs' channels. Improvement in the throughput is also achieved by manipulating the DCF uplink mode and the Downlink mode. A performance evaluation shows that the proposed scheme is superior to IEEE 802.11 WLAN in handoff delay and throughput.

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Cognitive radio (CR) in spectrum sharing mode allows secondary user (SU) to share the same spectrum simultaneously with primary user (PU), as long as the former guarantees no harmful interference is caused to the latter. This letter proposes a new type of constraint to protect the PU systems that are carrying delay-sensitive applications, namely the PU effective capacity loss constraint, which sets an upper bound on the maximum effective capacity loss of the PU due to the SU transmission. In addition, the PU effective capacity loss constraint is approximately transformed to the interference temperature (power) constraint, to make it easier to be implemented. As an example, we obtain a closed form expression of the SU effective capacity under the approximated peak interference power constraint and the results of simulations validate the proposed PU protection criterion.

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Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.

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This paper proposes a low power artificial bandwidth extension (ABE) technique that reduces computational complexity by introducing a fast codebook mapping method. We also introduce a weighted classified codebook mapping method for constructing the spectral envelope of the wideband speech signal. Classified codebooks are used to reduce spectrum mapping errors caused by characteristic difference among voiced, unvoiced and onset sound. The weighted distortion measure is also used to handle the spectral sensibility. The performance of the proposed ABE system is evaluated by a spectral distortion (SD), a perceptual evaluation of speech quality (PESQ), informal listening tests and weighted million operations per second (WMOPS) calculations. With the use of fast codebook mapping, the WMOPS complexity of the codebook mapping module is reduced by 45.17%.

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