|1||2013-08-01||Compressive Sensing for Spread Spectrum Receivers||Hyeongho||(pdf)|
|This paper investigates the use of Compressive Sensing(CS) in a general Code Division Multiple Access (CDMA) receiver. They show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. Furthermore, they numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures.|
|2||2013-08-08||Active illumination single-pixel camera based on compressive sensing||Eunseok||PPT|
This paper is organized as follows. After a brief introduction (Section 1), some mathematical back- ground essential to the understanding of CS is shown (Section 2). Then, in Section 3, CS is presented along with some of its principal properties. Section 4 explains why the ℓ1-norm is such a good option for com- pressive sensing. Some insights about the robustness of CS in the presence of noise are given in Section 5. Next, in Section 6, the single-pixel camera developed at Rice University is discussed. Subsequently, the innovative active illumination single-pixel camera developed in the scope of the current work is described. Following that, experimental results from the single-pixel cameras are presented. In the end, the main conclusions of this work are exposed.
|3||2013-08-29||Resource Allocation in Cognitive Radio Relay Networks||Muhammad Asif||(PDF)|
|In this paper authors formulate the problem of Resource Allocation (RA) in Cognitive Radio (CR) networks with relay stations. The problem takes into account the issues like: fluctuations of usable spectrum resource, channel quality variations caused by frequency selectivity, and interference caused by different transmit power levels. They propose easy to implement heuristic algorithms. The simulation results reveal that presented solutions show good proportional fairness among CR users and improvement in system throughput by power control.|
|4||2013-09-16||Multipath Matching Pursuit||Hwanchol Jang||(pdf)|
|In this paper, they propose an algorithm referred to as multipath matching pursuit that investigates multiple promising candidates to recover sparse signals from compressed measurements. Their method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the greedy search strategy is a good fit for solving this problem. In the empirical results as well as the restricted isometry property (RIP) based performance guarantee, they show that the proposed MMP algorithm is effective in reconstructing original sparse signals for both noiseless and noisy scenarios.|