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UNIRM – Research Activities

UNIRM – Research Activities. Generalized Access for MIMO Cognitive Radios. UNIRM – Research Activities. Why Cognitive? Cognitive Radio is the enhancement of Software-Radio and it defines the self-reconfiguration as paradigm. UNIRM – Research Activities.

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UNIRM – Research Activities

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  1. UNIRM – Research Activities Generalized Access for MIMO Cognitive Radios WOMEN project, Final Meeting

  2. UNIRM – Research Activities Why Cognitive? Cognitive Radio is the enhancement of Software-Radio and it defines the self-reconfiguration as paradigm WOMEN project, Final Meeting

  3. UNIRM – Research Activities Access systems mainly based their mechanisms by basing them on a priori “choice” This means: CDMA, TDMA or ALOHA etc. WOMEN project, Final Meeting

  4. UNIRM – Research Activities This approach constrains the architecture and does not allow the general user to access by following the best access policy What possible way? WOMEN project, Final Meeting

  5. UNIRM – Research Activities NOT TO CHOOSE a strategy when the system is planned. We decide to choose (assign) only few parameters (possible spreading factor, number of time slots, number of subcarriers) WOMEN project, Final Meeting

  6. UNIRM – Research Activities The general links can be represented as WOMEN project, Final Meeting

  7. UNIRM – Research Activities Analytically the system model is given by WOMEN project, Final Meeting

  8. UNIRM – Research Activities Each element of matrix Y is a matrix given by WOMEN project, Final Meeting

  9. UNIRM – Research Activities The statistical features of interference can be gathered in WOMEN project, Final Meeting

  10. UNIRM – Research Activities Estimation phase: Symbols used to acquire information about channel Payload phase: WOMEN project, Final Meeting

  11. UNIRM – Research Activities Objective functions WOMEN project, Final Meeting

  12. UNIRM – Research Activities The access strategy able to guarantee the QoS level exists if and only if WOMEN project, Final Meeting

  13. UNIRM – Research Activities Select the minimum over q-index and slot (time, frequency, code…) In this respect we have two different approaches, one by considering channel knowledge and the other without considering channel information WOMEN project, Final Meeting

  14. UNIRM – Research Activities TDMA no CSI TDMA CSI WOMEN project, Final Meeting

  15. UNIRM – Research Activities FDMA no CSI FDMA CSI WOMEN project, Final Meeting

  16. UNIRM – Research Activities CDMA SDMA CSI WOMEN project, Final Meeting

  17. UNIRM – Research Activities System performance WOMEN project, Final Meeting

  18. UNIRM – Research Activities System performance WOMEN project, Final Meeting

  19. UNIRM – Research Activities This approach outperform conventional approaches and it is really simple to implement Future hints deal with dynamic access strategies to be taken into account WOMEN project, Final Meeting

  20. Video over Wireless Mesh NetworksS. Colonnese, G. Panci, S. Rinauro, G. Scarano • Modeling of an H.264 Source adopting bitstream switching • Innovative SP coding scheme • Video codec changes its characteristics (output rate, video packet size) as it switches from a rate to another • Model Verification • Measurement of the Visual Relevance of Encoded Data • Rate Distortion Analysis in the Circular Harmonic Functions Domain

  21. Modeling of an H.264 Source Performing Bitstream Switching • Previous Work • Static modeling: distributions for P, primary SP and secondary SP frames • Dynamic modeling: synthetic VBR source modeling bitstream switching • Markovian Model (MM): each state represents the transmission of a frame (I, P, B and SP) • No interframe correlation • Development • Dynamic modeling • Markov Chain where each state models the generation of an entire Group Of Pictures (GOP) • Interframe correlation, typical of video sources taken into account by the interstate dependence

  22. Markov Model of H.264 Video Sources (1/4) • Markov Model • One state for each kind of GOP • Different GOPs depending on the kind of SP frames • A source performing bitrate switching among the rates i = 1, · · · , Lpresents alternative GOP structures:

  23. Markov Model of H.264 Video Sources (2/4)

  24. Markov Model of H.264 Video Sources (3/4) • Ngop-dimensional random variable representing the sizes of the frame of the GOP. • Correlation between the size of a frame and the sizes of the npreceeding frames • For Ngop< nthe state machine satisfies the first order Markovian property

  25. Markov Model of H.264 Video Sources (4/4) • Model for the variateX: • Aldependence with the preceeding GOP (only the the last n componentsare nonzero) • Bldependence of each variate Xi [n] with the preceeding variates belonging to the same GOP. • Cldrives the expected value of X [n] • Elzero-mean suitably distributed random vector with statistically independent components

  26. Model Validation via Buffer loading

  27. Experimental Results: bit switching • Real H.264 source (blue), MM (red) and AR (black) synthetic sources

  28. Measurement of the visual relevance of encoded data • Video Streaming over WMNs • Delay constrains (few re-transmissions) • No adaptive encoding (different users share the same data) • Data loss due to hadeover between heterogeneous netowrks • Decoded quality can be improved by: • Differentiating the error protection on different kinds of coded video data • Optimizing the resource allocation at the enocding side Measurement of the visual relevance of encoded data • State of the art distortion measurement  MSE (inaccurate)

  29. Y(0)[m, n] F(0)[m, n] + Y(1)[m, n] F(0)[m, n] Circular Harmonic Functions

  30. Encoding error Due to the lossy behaviour of the encoding process Same MSE Packet loss error Due to the loss of a slice of the frame Rate Distortion Analysis • RD analysis jointly in the domain of the CHFs of order 0 and 1.

  31. Application to Video Streaming System • Each frame of the sequence is partitioned in K slices • For each slice the following quantity is evaluated: • The values of rican be interpreted as a measurement for the visual relevance of each slice • Each slice is protected according to its visual relevance.

  32. Major visual distortion CHF measurement MSE measurement Experimental Results • Foreman; QCIF format; 120 Kbps; K=3 slice per frame

  33. Major visual distortion CHF measurement MSE measurement Experimental Results • Carphone; QCIF format; 120 Kbps; K=3 slice per frame

  34. Conclusion • Modeling of an H.264 Source adopting bitstream switching • Video codec changes its characteristics (output rate, video packet size) as it switches from a rate to another • Markov source model based on video flow structure • Model Verification • Transmission buffer cell loss rate S.Colonnese, G. Panci, S. Rinauro, G. Scarano, “Markov Model of H.264 Video Sources Performing Bit-Rate Switching”submitted to ICIP 2008 • Measurement of the Visual Relevance of Encoded Data • Visual Relevance measurement to drive differentiated protection • Rate Distortion Analysis in the Circular Harmonic Functions Domain • Future work • Employ visual relevance information to drive the error concealment S.Colonnese, G. Panci, S. Rinauro, G. Scarano, “Rate Distortion Analysis in the Circular Harmonic Functions Domain; an Application to Video Streaming”submitted to EUSIPCO 2008

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