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Baseband Signal

The Experts below are selected from a list of 6993 Experts worldwide ranked by ideXlab platform

Joseph R. Cavallaro – 1st expert on this subject based on the ideXlab platform

  • GLOBECOM – Baseband Signal compression in wireless base stations
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Aida Vosoughi, Michael Wu, Joseph R. Cavallaro

    Abstract:

    To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of Baseband Signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of Baseband Signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed Baseband Signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5∶1 in downlink. Our simulations and over-the-air experiments suggest that compression of Baseband Signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.

  • Baseband Signal compression in wireless base stations
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Aida Vosoughi, Michael Wu, Joseph R. Cavallaro

    Abstract:

    To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of Baseband Signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of Baseband Signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed Baseband Signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5:1 in downlink. Our simulations and over-the-air experiments suggest that compression of Baseband Signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.

Aida Vosoughi – 2nd expert on this subject based on the ideXlab platform

  • GLOBECOM – Baseband Signal compression in wireless base stations
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Aida Vosoughi, Michael Wu, Joseph R. Cavallaro

    Abstract:

    To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of Baseband Signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of Baseband Signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed Baseband Signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5∶1 in downlink. Our simulations and over-the-air experiments suggest that compression of Baseband Signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.

  • Baseband Signal compression in wireless base stations
    2012 IEEE Global Communications Conference (GLOBECOM), 2012
    Co-Authors: Aida Vosoughi, Michael Wu, Joseph R. Cavallaro

    Abstract:

    To comply with the evolving wireless standards, base stations must provide greater data rates over the serial data link between base station processor and RF unit. This link is especially important in distributed antenna systems and cooperating base stations settings. This paper explores the compression of Baseband Signal samples prior to transfer over the above-mentioned link. We study lossy and lossless compression of Baseband Signals and analyze the cost and gain of each approach. Sample quantizing is proposed as a lossy compression scheme and it is shown to be effective by experiments. With QPSK modulation, sample quantizing achieves a compression ratio of 4:1 and 3.5:1 in downlink and uplink, respectively. The corresponding compression ratios are 2.3:1 and 2:1 for 16-QAM. In addition, lossless compression algorithms including arithmetic coding, Elias-gamma coding, and unused significant bit removal, and also a recently proposed Baseband Signal compression scheme are evaluated. The best compression ratio achieved for lossless compression is 1.5:1 in downlink. Our simulations and over-the-air experiments suggest that compression of Baseband Signal samples is a feasible and promising solution for increasing the effective bit rates of the link to/from remote RF units without requiring much complexity and cost to the base station.

Qing Wang – 3rd expert on this subject based on the ideXlab platform

  • gps software receiver Baseband Signal real time tracking method based on code memory
    , 2012
    Co-Authors: Yaochuan Cao, Weibo Ji, Shuguo Pan, Qing Wang

    Abstract:

    The invention discloses a GPS software receiver Baseband Signal real-time tracking method based on code memory, wherein the method is based on prestored and fine-adjusted local C/A code to perform binary system storage and performs table lookup and invocation to solve the problem that real-time code which wastes time are generated in GPS software receiver tracking Signal time. In GPS software receiver tracking loop, the process that local C/A code is generated through minute adjustment is longest in the three longest processes, namely generation of fine-adjusted local C/A code, generation of local carrier wave and related integration and the time for generating fine-adjusted local C/A code is nearly half of the entire Signal tracking and processing time. The invention uses the binary dataof prestored and fine-adjusted local C/A code and directly loads data to perform table lookup and call data so that the fine-adjusted early code and late code can not be generated in every tracking cycle, the time wasted by real-time code can be saved, the time for the generation of codes in tracking cycle is saved and the tracking speed doubles.

  • WiMAX Signal generation based on MIMO-OFDM testbed for passive radar application
    2009 4th IEEE Conference on Industrial Electronics and Applications, 2009
    Co-Authors: Qing Wang, Yilong Lu

    Abstract:

    In order to analyze the ambiguity function of WiMAX Signals and evaluate their suitability for passive radar, the first step should be the generation of WiMAX transmission Signal, including random data generation, modulation, Space Time Block Coding (STBC), preamble generation for synchronization and channel estimation, frame combination and Orthogonal Frequency Division Multiplexing (OFDM) modulation. In this paper, WiMAX Signal generation based on a Multiple-Input-Multiple-output (MIMO) OFDM testbed as well as detailed Baseband Signal processing algorithms are presented. Field trials and measurements based on the MIMO-OFDM testbed in different channel conditions have verified that the Signal generation method and the adopted Baseband Signal processing algorithms are effective.