OFDM : Why it’s a good fit for Cognitive Radio

The underlying sensing and spectrum shaping capabilities together with flexibility and adaptiveness make OFDM probably the best candidate for cognitive radio systems.

In this article I discuss Spectrum Sensing/ Awareness and Spectrum Shaping requirements for cognitive radios and explain how OFDM can fulfill these requirements.

(a) Spectrum Sensing and Awareness


Cognitive radio should be able to scan the spectrum and measure different channel characteristics such as power availability, interference, and noise temperature. In addition, the system should be able to identify different users’ signals in the spectrum and also identify if they are either licensed or rental users. These abilities allow cognitive radio system to identify unused parts of the spectrum and spectral opportunities. However, since for a rental system it is important not to interfere with other licensed systems using the spectrum, other measures should be taken to guarantee an interference-free communication between rental users. One approach is to share the spectrum sensing information between multiple cognitive radio devices to decrease or even eliminate the probability of interference with licensed users. On the other hand, more sophisticated algorithms can be used for sensing the spectrum.


While the efficiency of the spectrum sensing and analyzing process is important for a successful implementation of cognitive radio, the processing time can be even more important. The periodicity of spectrum sensing should be short enough to allow for detection of new spectrum opportunities and, at the same time, to detect licensed users accessing the previously-identified-as unused parts of the spectrum. On the other hand, if spectrum sensing is done so frequently, the overhead of sharing such information will increase reducing the spectrum efficiency of the whole system not to mention the increase in system complexity. In OFDM systems, conversion from time domain to frequency domain is achieved inherently by using DFT. Hence, all the points in the time–frequency grid can be scanned without any extra hardware and computation because of the hardware reuse of Fast Fourier Transform (FFT) cores. Using the time–frequency grid, the selection of bins that are available for exploitation (spectrum holes) can be carried out using simple hypothesis testing. The DFT outputs can be filtered across time and frequency dimensions to reduce the uncertainty in detection as well. Note that the resolution of the frequency grid is dependent on subcarrier spacing.


Spectrum Shaping


After a cognitive radio system scans the spectrum and identifies active licensed users and available opportunities, comes the next step: spectrum shaping. Theoretically, it is desired to allow the cognitive users to freely use available unused portions of the spectrum.


Cognitive users should be able to flexibly shape the transmitted signal spectrum. It is desired to have control over waveform parameters such as the signal bandwidth, power level, center frequency, and most of all a flexible spectrum mask. OFDM systems can provide such flexibility due to the unique nature of OFDM signaling. By disabling a set of subcarriers, the spectrum of OFDM signals can be adaptively shaped to fit into the required spectrum mask. Assuming the spectrum mask is already known to the cognitive radio system, choosing the disabled subcarriers is a relatively simple process


The main parameters of an OFDM system that can be used to shape the signal spectrum are number of subcarriers, subcarrier’s power, and pulseshaping filters. Increasing the number of subcarriers for a fixed bandwidth allows the OFDM system to have a higher resolution in the frequency domain. However, this results in increasing the complexity of the FFT operations and thus increasing the overall system complexity. Subcarrier power can be used to shape the signal into the desired mask. One reason to assign subcarriers different powers is to better fit into the channel response. For example, subcarriers with higher SNR values can be assigned lower power than those with lower SNR to improve the overall system BER. Another reason is to reduce the adjacent channel interference from an OFDM system by reducing the power assigned to edge subcarriers. An example of spectrum sensing and shaping procedures in OFDM-based cognitive radio systems is illustrated in Figure below;

Two licensed users are detected using the output of FFT block, and subcarriers that can cause interference to licensed user are turned off. The transmitter then uses the free parts of the spectrum for signal transmission. In addition, pulse-shaping filters can also be used to reduce the interference to adjacent bands.




In my upcoming articles I will discuss other requirements such as “Adapting to Environment”, “Advanced Antenna Techniques”, “Multiple Accessing and Spectral Allocation”, “Interoperability”.




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