12 research outputs found

    MIMO OFDM Radar-Communication System with Mutual Interference Cancellation

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    This work describes the OFDM-based MIMO Radar-Communication System, intended for operation in a multiple-user network, especially the automotive sector in the vehicle-to vehicle/infrastructure network. The OFDM signals however are weak towards frequency offsets causing subcarrier misalignment and corrupts the radar estimation and the demodulation of the communication signal. A simple yet effective interference cancellation algorithm is detailed here with real time measurement verification

    Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

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    Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions

    Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

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    This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

    Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment

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    This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

    Channel estimation and synchronization for orthogonal frequency division multiplexing with known symbol padding

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    An ML-Based Estimate and the Cramer-Rao Bound for Data-Aided Channel Estimation in KSP-OFDM

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    Abstract We consider the Cramer-Rao bound (CRB) for data-aided channel estimation for OFDM with known symbol padding (KSP-OFDM). The pilot symbols used to estimate the channel are positioned not only in the guard interval but also on some of the OFDM carriers, in order to improve the estimation accuracy for a given guard interval length. As the true CRB is very hard to evaluate, we derive an approximate analytical expression for the CRB, that is, the Gaussian CRB (GCRB), which is accurate for large block sizes. This derivation involves an invertible linear transformation of the received samples, yielding an observation vector of which a number of components are (nearly) independent of the unknown information-bearing data symbols. The low SNR limit of the GCRB is obtained by ignoring the presence of the data symbols in the received signals. At high SNR, the GCRB is mainly determined by the observations that are (nearly) independent of the data symbols; the additional information provided by the other observations is negligible. Both SNR limits are inversely proportional to the SNR. The GCRB is essentially independent of the FFT size and the used pilot sequence, and inversely proportional to the number of pilots. For a given number of pilot symbols, the CRB slightly increases with the guard interval length. Further, a low complexity ML-based channel estimator is derived from the observation subset that is (nearly) independent of the data symbols. Although this estimator exploits only a part of the observation, its mean-squared error (MSE) performance is close the CRB for a large range of SNR. However, at high SNR, the MSE reaches an error floor caused by the residual presence of data symbols in the considered observation subset.</p

    Research Article An ML-Based Estimate and the Cramer-Rao Bound for Data-Aided Channel Estimation in KSP-OFDM

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    We consider the Cramer-Rao bound (CRB) for data-aided channel estimation for OFDM with known symbol padding (KSP-OFDM). The pilot symbols used to estimate the channel are positioned not only in the guard interval but also on some of the OFDM carriers, in order to improve the estimation accuracy for a given guard interval length. As the true CRB is very hard to evaluate, we derive an approximate analytical expression for the CRB, that is, the Gaussian CRB (GCRB), which is accurate for large block sizes. This derivation involves an invertible linear transformation of the received samples, yielding an observation vector of which a number of components are (nearly) independent of the unknown information-bearing data symbols. The low SNR limit of the GCRB is obtained by ignoring the presence of the data symbols in the received signals. At high SNR, the GCRB is mainly determined by the observations that are (nearly) independent of the data symbols; the additional information provided by the other observations is negligible. Both SNR limits are inversely proportional to the SNR. The GCRB is essentially independent of the FFT size and the used pilot sequence, and inversely proportional to the number of pilots. For a given number of pilot symbols, the CRB slightly increases with the guard interval length. Further, a low complexity ML-based channel estimator is derived from the observation subset that is (nearly) independent of the data symbols. Although this estimator exploits only a part of the observation, its mean-squared error (MSE) performance is close the CRB for a large range of SNR. However, at high SNR, the MSE reaches an error floor caused by the residual presence of data symbols in the considered observation subset

    The Cramer-Rao bound and ML estimate for data-aided channel estimation in KSP-OFDM

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    In this paper, we derive the Cramer-Rao bound (CRB) for data-aided channel estimation for OFDM with known symbol padding (KSP-OFDM). The pilot symbols used to estimate the channel are distributed over the guard interval and OFDM carriers, in order to keep the guard interval length as small as possible. An analytical expression for the CRB is obtained by performing a proper linear transformation on the observed samples. At low SNR, the CRB corresponds to the low SNR limit of the CRB obtained in [1], where it is assumed that the influence of the data symbols on the channel estimation can be neglected. At high SNR, the CRB is determined by the observations that are independent of the data symbols; the observations that are affected by data symbols are neglected. The CRB depends on the number of pilots and slightly increases with increasing guard interval length, but is essentially independent of the FFT size and the used pilot sequence. Further, a low complexity ML channel estimation technique is derived based on the linear transformation. Although in this estimation technique only a part of the observation is used, the mean squared error (MSE) performance of this estimate reaches the CRB for a large range of SNR, but a high SNR, the MSE reaches an error floor caused by the approximations made in the derivation
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