Centre for Wireless Research (CWR)The Centre for Wireless Research brings together expertise in the
areas of mobile and wireless sensor networks. The breadth and depth
of the expertise make the Centre rich with research and innovation
potential.http://hdl.handle.net/10547/1322312024-03-16T12:10:46Z2024-03-16T12:10:46ZOn statistical characterization of EESM effective SNR over frequency selective channelsHui SongKwan, R.Jie Zhanghttp://hdl.handle.net/10547/5936872020-04-23T07:33:22Z2009-08-01T00:00:00ZOn statistical characterization of EESM effective SNR over frequency selective channels
Hui Song; Kwan, R.; Jie Zhang
A novel expression of the moment generating function (MGF) of the exponential effective SNR mapping (EESM) signal-to-noise ratio (SNR) over two correlated but not necessarily identically distributed Nakagami-m fading channels is presented. Based on the MGF, a novel expression for the average effective SNR is also presented. Numerical evaluation of these expressions shows that the proposed approach can be a useful and efficient analytical tool in analyzing the characteristics of EESM over correlated Nakagami-m fading channels.
2009-08-01T00:00:00ZApproximations of EESM effective SNR distributionSong, HuiKwan, RaymondZhang, Jiehttp://hdl.handle.net/10547/5935392020-04-23T07:33:22Z2011-02-01T00:00:00ZApproximations of EESM effective SNR distribution
Song, Hui; Kwan, Raymond; Zhang, Jie
The Probability Density Function (PDF) or Cumulative Distribution Function (CDF) of the effective Signal to Noise Ratio (SNR) is an important statistical characterization in the performance analysis of an Orthogonal Frequency Division Multiple Access (OFDMA) system using Exponential Effective SNR Mapping (EESM). However, the exact closed form of PDF is extremely difficult to obtain. A general approximation method known as Moment Matching Approximating (MMA) is used to approximate the distribution of effective SNR by a simple expression. In this paper, the approximation by Gaussian, Generalized Extreme Value (GEV) and Pearson distribution are studied. Results show that Gaussian approximation is very useful when the number of sub-carriers is sufficiently large. Both GEV and Pearson approximation are accurate enough in approximating the distribution of effective SNR in a general case.
2011-02-01T00:00:00ZDisparity map compression for depth-image-based renderingCheung, GeneOrtega, AntonioKim, Woo-ShikVelisavljević, VladanKubota, Akirahttp://hdl.handle.net/10547/5582472017-02-23T10:01:52Z2012-03-01T00:00:00ZDisparity map compression for depth-image-based rendering
Cheung, Gene; Ortega, Antonio; Kim, Woo-Shik; Velisavljević, Vladan; Kubota, Akira
2012-03-01T00:00:00ZJoint source and channel coding of view and rate scalable multi-view videoChakareski, JacobVelisavljević, VladanStankovic, Vladimirhttp://hdl.handle.net/10547/5582452020-04-23T07:35:17Z2014-10-01T00:00:00ZJoint source and channel coding of view and rate scalable multi-view video
Chakareski, Jacob; Velisavljević, Vladan; Stankovic, Vladimir
We study multicast of multi-view content in the video plus depth format to heterogeneous clients. We design a joint source-channel coding scheme based on view and rate embedded source coding and rateless channel coding. It comprises an optimization framework for joint view selection and source-channel rate allocation, and includes a fast method for separate optimization of the source and channel coding components, at a negligible performance loss wrt the joint solution. We demonstrate performance gains over a state-of-the-art method based on H.264/SVC, in the case of two client classes.
2014-10-01T00:00:00Z