1. Elad Hever & Elad Kaner June 2004 by: BER PREDICTION IN WIRELESS COMMUNICATION LINKS WITH APLLICATION TO MULTIMEDIA NETWORKS Tutor Names: Tutor Names: Pro. Nathan Blaunstein Pro. Nathan Blaunstein& Dr. Shlomo Greenberg

2. INTRODUCTION In the very near future the next generation of wireless communication, the 3G, will become integrated part of our life. The 3G, is envisioned to enable communication at any time, in any place, with any form, as such, it will: The 3G, is envisioned to enable communication at any time, in any place, with any form, as such, it will: –allow global roaming –provide for wider bandwidths to accommodate different types of applications –support packet switching concepts

3. UMTS is denoted as the 3G cellular system and is developed within the framework that has been defined by the International Telecommunications Union (ITU). UMTS has been designed with the objective to be a system with global coverage. UMTS will support high bit-rate data services of 144 Kbit/s to 2 Mbit/s depending on the radio environment (Macro, Micro, and Pico cells). UMTS (Universal Mobile Telecommunication System)

4. Cell types in UMTS: 3 rd Generation Satellite Global Macro cell Suburban Micro cell Urban In-Building Pico cell

5. UMTS services: The data rates that will be offered by the UMTS standard will make it possible to introduce a great deal of new applications. UMTS will be able to support real time services including multimedia as well as packet data services.

6. Project scope Transmission of data that requires high quality is a major issue in wireless networks. This type of network tends to be error prone due to certain atmospherically disturbances (e.g. attenuation, reflection or diffraction), which affect the transmitted signal. In this project we examine UMTS channels and transmission of Multimedia services through these channels.

7. Objective BER evaluation of W-CDMA in a variety of channels, such as the additive white Gaussian noise (AWGN), the Rayleigh fading, and the Rician fading channel. Simulate the performance of that channels as a function of signal-to-noise ratio (SNR). And finally present how the various kind of noise can affect the quality of a Real-Time Multimedia service.

9. The System Model Including: 1) Transmitter 2) Channels 3) Receiver

10. Transmitter Model

11. where: and the transmitted signal is:

12. The Channels: We consider three channel models : AWGN, Rayleigh fading and Rician fading channel. 1) AWGN Channel: This channel is assumed to corrupt the signal by the addition of white Gaussian noise, n(t), which denotes a sample function of the additive white Gaussian noise process with zero-mean and two-sided power spectral density

13. 2) Rayleigh Fading Channel: In this channel, the transfer function assumed for the m’th user can be represented as: The random magnitude is assumed to be (iid) Rayleigh random variables for all users and sub-carriers, where Rayleigh distribution is:

14. 3) Rician Fading Channel: If we consider line-of-sight (LOS) with magnitude b 0 =const, the transfer function assumed for the m’th user can be represnted as: The NLOS magnitude factors are assumed to have the following Rician distribution:

15. Receiver Model

16. System Performance 1)AWGN Channel In AWGN channel the received signal is: By the receiver, the decision variable is:

17. By product of trigonometric functions: The first term represents the bit of k’th desire user, the second represents the multiple-access interference and the third term represents AWGN.

18. The interference term may be written as: where: so The mean of is:

19. The variance of is: The error probability is:

20. 2) Rayleigh fading channel In Rayleigh channel the received signal is: By the receiver, the decision variable is:

21. After simplifiy the equation we get: The variance of the interference term is:

22. The mean of the desired data term is: The variance of the desired data term is: The variance of the noise term is: The mean of is:

23. The variance of is: and the approximated BER is:

24. 3) Rician fading channel In Rician channel the decision variable is: where are Rician distribution with mean of:

25. The mean of the interference term is: The variance of the interference term is:

26. The mean of the desired data term is: Therefore, the mean of is:

27. The variance of is: The approximated BER is:

28. Numerical Results In this section, we analytically simulate the performance of Multicarrier CDMA explained above sections. The simulation was done on MATLAB platform.

29. The performance in various channels

30. The performance in Rayleigh channel for various values of M

31. The performance in Rayleigh channel for various values of N

32. The performance in Rician channel for various values of M (K=10)

33. The performance in Rician channel for various values of N (K=10)

34. Video performance experiments In order to comprehend the effect of the various noise factors on a transmitted video sequence, we created a simulation environment. The simulation environment emulate the wireless channel, as such it has the capability of generating bit errors, burst errors, packet loss and random bit errors. The video sequence used in the experiments is the Akiyo video sequence. This sequence is regarded as a low-motion sequence, typical for videoconferencing. We have manipulated the sequence using the MPEG-4 video codec, which was selected as a multimedia standard for the 3rd generation wireless network.

35. Evaluation methods: Two methods were singled out to measure the quality of the degraded video sequence after “transmission” into the wireless channel: 1. Distortion Measure 2. Subjective evaluation

36. Distortion Measure Generally, the Root Mean Square Error (RMSE) is used as a measure to assess the video degradation due to noise that is present in the radio channel. To compute the value of RMSE, the video sequence has to be divided into grey scale (8 bit) frames of size NxM pixels Then the original pixel frame is compared with the degraded one: Where, f(x, y)1 – is the original frame f(x, y)2 – is the degraded frame

37. Distortion Measure (2) The common approach to measure quality of video is to calculate the Peak Signal to Noise Ratio (PSNR).

38. Distortion Measure (3) Encode with MPEG-4 Original video sequence Filter - Simulate noise Segmentation Decode with MPEG-4 Compare original with degraded By calculation of PSNR

39. Subjective evaluation Another important aspect in evaluating the quality of the video is to perform a subjective assessment on the resulting degraded video sequence. calculated PSNR values do not enlighten without a person’s view of the difference between the original and the degraded video sequence.

40. Subjective evaluation (2) Encode with MPEG-4 Original video sequence Filter - Simulate noise Decode with MPEG-4 Compare original with degraded By subjective assessment Playback

41. Simulation parameters: Video codec Microsoft MPEG-4 Video codec Reference Software Encoding rate 400kbps Video sequence Akiyo/qcif test sequence (300 frames at 30 fps / no audio) Error Rates Constant BER 10^(-3) 10^(-3) 10^(-4) 10^(-4) 10^(-5) 10^(-5) 10^(-6) 10^(-6) Burst BER 10^(-3) 10^(-3) 10^(-4) 10^(-4) 10^(-5) 10^(-5) 10^(-6) 10^(-6) Gaussian BER 10^(-3) 10^(-3) 10^(-4) 10^(-4) 10^(-5) 10^(-5) 10^(-6) 10^(-6) Packet loss

42. Simulation Results: Constant BER: As anticipated, both the objective and subjective results became worse when the BER increased. Quality severely degrades when the BER becomes larger than 10^(-5). No or little degradation of the video signal occurs at BER of 10^(-6). For BER 10^(-5), the signal returns to the “optimal” value of periodically. BER 10^(-4)BER 10^(-5)BER 10^(-6)

43. PSNR Results for Constant BER:

44. Burst errors: Comparing it with the former case, we can see that errors are more visible at BER 10^(-5) and 10^(-6) due to sequential errors in the bit stream. In real channel we can find this kind of errors due to interferences that result from stormy weather, when errors appear in “chunks”. We can also see the different PSNR results: BER 10^(-4)BER 10^(-5)BER 10^(-6)

45. PSNR Results for Burst Errors:

46. Gaussian noise: Inserting bit errors randomly may cause that all the bit errors are located beside each other. Our results show that from the subjective view of point the quality of the playback was some where between the first case and the second case. However the average PSNR results were similar to the PSNR results of the first case rather to be degraded like the playback did. BER 10^(-4)BER 10^(-5)BER 10^(-6)

47. PSNR Results for Gaussian BER:

48. Summary: The simulation analysis gives a basic comprehending what effects error propagation in wireless mobile channels can have on an MPEG4 encoded video stream (without error resilience). The highly compressed video data is extremely vulnerable towards transmission error. Low bit-rate video coding schemes rely on inter- frame coding to achieve high coding efficiency. Consequently, the loss of one transmitted video frame has a major impact on the quality of the following video frames. Consequently, the loss of one transmitted video frame has a major impact on the quality of the following video frames.

49. Summary (2): Of all the services available for the 3rd generation multimedia systems, transmission of Real-Time video is the most demanding one in terms of bandwidth and transmission delay. In addition, this service must also maintain certain QoS requirements. The videoconferencing and video streaming services shall work under conditions of BER 10^(-3), (according to 3GPP). Bearing this in mind, the simulation results show that the need for error robustness is enormous for transmissions over mobile networks. Error correction methods in form of retransmission may be used to contend the error propagation in the mobile network, but in video services like videoconferencing, the requirements to delay are strict so any form of retransmission is not wanted.

50. MPEG4 provide FEC (forward error correction methods), however, because transmission errors in heterogeneous mobile channels can range from single bit errors, burst errors, packet loss or maybe completely loss signal, the FEC may not be efficient. The codec also has some error resilience tools, but they were not tested in our simulation. It may be considered and experimented in further study. Summary (3):