The work in this project report has not previously been submitted for a degree or examination at any other higher education institution. I would like to thank all those who have helped and supported me during this project. Without his help I would not have been able to complete this project to its current standard.
I would also like to thank my friends, my family and my loved ones, Noor Melissa Putri for their endless support during the realization of this project.
Introduction
UI| Chapter 1 Introduction - 12 - The development of the IEEE 802.22 WRAN standard (4) aims to use cognitive radio techniques to allocate the part of geographically unused spectrum allocated to the television broadcasting service on a non-interference basis late to bring broadband access to hardware. -to reach low-population-density areas typically of rural environments, and is therefore timely and has the potential for wide applicability worldwide. IEEE 802.22 WRANs are designed to operate in the TV broadcast bands while ensuring that no harmful interference is caused to the established industry (i.e. digital TV and analog TV broadcasts) and low-power licensed devices such as wireless microphones. The student contribution of this report, including the comprehensive literature survey on spectrum sensing for cognitive radio, as well as detailed study for IEEE 802.22 (WRAN), brief overview on IEEE 802.22 Standard and proposing a fast sensing method for MAC layer in WRAN by wave energy detector to use.
The application of cognitive radio in 802.22 and related works proposed by the IEEE 802.22 WG is explained in Section III.
Cognitive Radio
Introduction
Main Functions of Cognitive Radio
- Spectrum Sensing
- Spectrum Management
- Spectrum Sharing
- Spectrum Mobility
Thus, a cognitive radio should be able to determine whether a primary transmitter signal is locally present in a given spectrum. However, the detection of matched filtering requires the cognitive radio to demodulate the signals and therefore requires a complete knowledge of the primary user's signaling functions such as bandwidth, operating frequency, modulation type and order, pulse shaping, frame format, etc. Moreover, since the cognitive radio needs receivers for all types of signals, the complexity of the implementation of the sensor unit is inappropriately high.
Therefore, the cognitive radio should only rely on weak signals of the primary transmitter based on the user's local observation. A cognate radio transmitter may have a good line of sight to a receiver, but may not be able to detect the transmitter due to shadowing. In the centralized method, the cognitive radio base station plays a role to collect all sensory information from users and detect spectrum holes.
In the distributed method, the cognitive radio base station requests the exchange of observations among the cognitive radio users. Since users are equipped with the physical layer based on cognitive radio, it is important to understand the characteristics of different spectrum bands. Existing solutions for spectrum allocation in cognitive radio can be mainly classified in three aspects: i.e., according to their architecture assumption, spectrum allocation behavior and spectrum access technique as shown in fig 4.
The analysis of cognitive radio spectrum sharing techniques has been explored through two main theoretical approaches. Cognitive radio networks strive to use the spectrum in a dynamic way by operating the radio terminals, also known as cognitive radio, in the best available frequency band.
WRAN (802.22)
Introduction
Where the base station (BS) manages its own cell and all associated consumer premises equipment (CPEs6). 5Point-to-multipoint communication is a term used in the field of telecommunications that refers to communication carried out over a specific and separate type of multipoint link that provides multiple paths from one location to multiple locations. UI| Chapter 3 WRAN One of the key features of WRAN base stations is that they will be able to perform distributed sensing.
This is that the CPEs will observe the spectrum and send periodic reports to the BS informing it of what they are observing. The BS, with the collected information, will evaluate whether a change is needed in the used channel, or on the contrary, whether it should continue to transmit and receive on the same channel. user interface| Chapter 3 WRAN Orthogonal Frequency-Division Multiple Access (OFDMA), a multi-user version of Orthogonal Frequency-Division Multiplexing (OFDM) digital modulation scheme, will be the modulation scheme for transmission in Uplink and Downlink.
This allows the system to have higher bandwidth which will be reflected in better system performance. At the PHY Layer, the channel connection uses more than one channel allowing the system to have a larger bandwidth and higher throughput. These will be sent by the BS on any channel that is possible to transmit and not cause interference.
When a CPE is switched on, it will sense the spectrum, find out which channels are available and will receive all the necessary information to attach to the BS. Fine sensing (about 25 ms per channel) is done if the BS feels that more detailed sensing is needed.
Fast Spectrum Sensing Algorithm for 802.22 WRAN Systems
Related Works in 802.22 Standard
- Energy Detection
- Fine/feature Sensing
UI| Chapter 4 Fast Spectrum Sensing Algorithm for 802.22 WRAN Systems - 29 - - Received Signal Strength Measurement (RSSI): This is a scheme that selects unoccupied. One of the possible implementations is the conversion of the energy in the band of interest into the power of the input signal or Multi-Resolution Spectrum Sensing (MRSS): MRSS is a scheme that can detect the band of interest in the analog domain using a wavelet transform, which can be the basis of the Fourier transform.
Since the energy detection schemes are performed in the wide band and must compare their results with a specific threshold, fast sensing and determination of the threshold are important parameters of the energy detection schemes. A common disadvantage of these schemes is that we have to know the functions of possible incoming signals in advance. The student does not specifically explain about this scheme because our concern is energy detection, fast registration detection.
Wavelet Based Energy Detector
- Power Measurement using Wavelet
- Complexity of Wavelet analysis
- The Algorithm Description
- The anticipated performance of the Algorithm
This means that the power of each subband can be calculated using scale and wavelet coefficients. Analysis of the number of mathematical operations, considering only multiplication, shows the complexity of the schemes. In discrete wavelet transform there is log2 N level decomposition and only the output of low pass filter goes through the next operation.
In the discrete wavelet packet transform, the outputs of the high-pass filter undergo the following processing. The discrete wavelet packet transform can separate the given frequency band into a low frequency subband and a high frequency subband. We also assume that the ratio between Bi and Bc is a power of 2. The procedure of the idea, wavelet-based energy detector, is shown in Figure 11. UI| Chapter 4 Fast Spectrum Sensing Algorithm for 802.22 WRAN Systems - 32 - Figure 11 The flowchart of the wavelet-based energy detector.
RI represents required iteration number for wavelet packet transform and is calculated by log2 (Bi). If not, the 1-level discrete wavelet packet transform is performed again with an increment of the iteration parameter by 1. 4) Calculate the power of each channel. This is achieved by sorting the channels in ascending order based on the power of each channel and it is rational due to the fact that the low power channel has high probability because it is an unoccupied channel.
Specifically, it performs discrete wavelet packet conversion not to the finite level but to the RI level, and its complexity becomes Where RI is the required iteration number of the discrete packet wavelet transform, L and N are the length of the wavelet filter and the input signal, respectively.
Simulations and Result
Simulation Environment
Since there are 16 channels in Bi, B is 100 KHz and a 4-level discrete decomposition of the waltz packets must be performed. From this figure, we can conclude that if the center frequency of the primary user is in the frequency band 0-100 KHz, the power of channel 1 must be greater than the power of other channels. A 34;db20" wavelet is used in the simulation and Figure 13 shows the size characteristic of the wavelet filter.
Implementation of Algorithm in Matlab Codes
UI| Chapter 5 Simulations and Result - 37 - The 1-wavelet packet transform 10 is performed in the received signal to generate the figure above. We can see that with the frequency changes in f3, the power in each subchannel varies according to the frequency. The tree decomposition showing how the Discrete Wavelet Transform method works is shown in the figure below. Wavelet packet atoms are waveforms indexed by three naturally interpreted parameters: position and scale as in wavelet decomposition and frequency.
Adaptive filtering algorithms with direct applications in optimal signal coding and data compression can then be produced. In the orthogonal wavelet decomposition procedure, the generic step divides the approximation coefficients into two parts. After splitting, we get a vector of approximation coefficients and a vector of detail coefficients, both on a coarser scale.
In the corresponding wave packet situation, each detail coefficient vector is also decomposed into two parts with the same approximation as in approximation vector partitioning. It offers the richest analysis: the complete binary tree is produced in the one-dimensional case or a quaternary tree in the two-dimensional case.
Simulations for Test Cases using Matlab
As shown in the figure above, channel 2 has the lowest power, while channel 5 has the highest power in dB, which means that there is a high probability that channel 2 is not used while channel 5 is more likely to be used . We can see the ordered channel in Figure 18 and the frequency response for the "dB20" wavelet filter in Figure 19. This makes it possible for the proposed scheme to select unoccupied channel candidates without confirming whether the spectra are unused or not.
From the above simulation results, moreover, the proposed scheme is verified to identify the channels in which the main users exist. Since RI = 4 in these simulations, the complexity of the proposed scheme is 8LN using Equation 5. UI| Chapter 5 Simulations and result - 42 - Applying the simulation using discrete wavelet transform at level 1 in Matlab, we can see that the power in 16 channels varies for each channel as shown in figure 17.
UI| Chapter 5 Simulations and Results - 43 - As shown in the figure above, channel 2 has the lowest power, while expected channel 1, 10 and 15 are greater than other channels, which means that there is a high probability that channel 2 will not be transmitted by user is not used while channel 1, 10 and 15 are larger than other channels and most likely to be used.
Summaries and Conclusion
Implementation of Algorithm for Wavelet Packet Transform
Simulation for Test Case 1
Simulation for Test Case 2
