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With the cancellation in place, it is possible to implement the collision detection algorithm. A thorough theoretical background and comparison of the algorithms is provided in Chapter8, here we will detail the prototype and its performance. Using the system of Figure5.6, the detection problem can be written as a binary hypothesis,
Yn= (
hSIXn+ Wn if H0
hSIXn+ hiXi+ Wn if H1, (5.1) where hSIXnis the remaining self-interference received by the IBFD transmitter, Wn is the noise, and hiXi is the interfering signal received by the IBFD transmitter. The two hypotheses can be defined as H0 being the case when no
collision is occurring and H1being the case when there is a collision.
To differentiate between the two hypotheses, we use a goodness-of-fit test called the Kuiper test [74]. A goodness-of-fit test calculates the distance between two empirical cumulative distribution functions (CDF). When this distance is above a certain threshold, the null hypothesis is rejected. In the case of the Kuiper test, this distance is defined as
TKP= sup
τ { ˆFY(τ) − ˆFY0(τ)}
+ sup
τ { ˆFY0(τ) − ˆFY(τ)} > λKP,α, (5.2)
where ˆFY0(τ)is the reference empirical cumulative distribution function (CDF)
THE IN-BAND FULL DUPLEX PROTOTYPE 63 0 5 10 15 20 25 30 35 40 45 50 0 0.2 0.4 0.6 0.8 1
Difference between interferer and remaining SI [dB]
Detection
probabilit
y
Figure 5.7: Measured detection probability using our collision detection prototype. (Source: [75])
interferers present. ˆFY(τ)is the empirical CDF of the current received samples
and λKP,α is the threshold which can be trained in advance.
This entire algorithm is implemented on the FPGA of a USRP. Chapter10
provides a more in depth explanation of the implementation. For now, let us look at the detection performance of the algorithm, shown in Figure5.7. Given the setup in Figure5.6, the algorithm is able to detect collisions and interferers with a signal power which is 20 dB below the remaining self-interference with a close to 100% reliability. Throughout these measurements, the false alarm rate was below 5%. This is, to the best of our knowledge, the first real-time implementation able to detect in-band collisions at the transmitter under self- interference.
To put these numbers into perspective, let us assume an analog self-interference cancellation of 60 dB, a transmit power of 0 dBm and a noise floor of -90 dBm. The level diagram in Figure5.8shows the range of detectable interferers. This means that any collision or interferer with a power between 0 dBm and -80 dBm can be detected. Increasing the analog cancellation or adding digital cancellation can decrease the lower bound up to the noise floor, as in [72] we have shown that the detection algorithm is limited by the noise floor.
0 −60 −80 −90 P o w er [dBm] TX power @ TX Remaining SI @ RX Minimum interferer @ RX Noise power @ RX Detectable interferers
Figure 5.8: All power levels and the detectable power of the interferers for our detection algorithm. (Source: [69])
5.3
Conclusion
This chapter presents an overview of prototyping techniques for PHY and MAC layer research. The PHY layer community typically uses software defined radios as these give them the flexibility needed. The MAC layer community doesn’t need this flexibility but requires real-time performance from the prototype to test their protocols in a network of devices. To do cross-layer research with either of these approaches is difficult as for this type of research, flexibility with real-time performance is needed. Some platforms are already starting to offer this but support is still rather thin.
Therefore, in the second part of this chapter, our prototype using in-band full duplex is presented. The prototype is built on a USRP with a big FPGA onboard. The FPGA takes care of all the real-time requirements as well as the flexibility needed to develop novel PHY layer algorithms. Using this approach a prototype was built which uses IBFD to cancel the self-transmitted signal and then uses its receiver chain to detect collisions during a transmission. This novel PHY layer feature is then used by the MAC layer to optimize the channel access performance.
The prototype is used in Chapter10to form a network of six IBFD-enabled software defined radios. To the best of our knowledge this is the first time IBFD is tested in a network setting. Moreover, it is the first measurement of collision detection in a wireless network.
Chapter 6
Conclusions and future work
In this final chapter, the doctoral work is summarized as well as some future work is discussed. First, the individual conclusions of the main papers that are bundled in the next chapters are summarized, followed by an overall conclusion of this work. Furthermore, the next steps in terms of future work are presented later in this chapter.
6.1
Paper conclusions
In the second part of this doctoral work, four main papers are presented. The first paper (Chapter7) concerns a full theoretical analysis of the performance improvements of collision detection using in-band full duplex. The second paper (Chapter8) investigates and compares techniques to enable collision detection at the transmitter where the received signal is affected by self-interference. The third paper (Chapter 9) details the basis of our prototyping platform which enables cross-layer research. Finally, the fourth paper (Chapter 10) gives a comprehensive look at our full prototype which uses in-band full duplex to enable collision detection. Moreover, the improvements are validated in a network of connected software defined radios. We first summarize and conclude these papers before going to the main conclusions and future work.