In this section, we study the effect of multipath propagation on the performance of the proposed system. Fig. 5.7(a) displays the average power delay profile of the UWB channel model mentioned in [136], which is used throughout the simulations. The excess delay is measured relative to the first arrival, and the vertical axis denotes the energy level of each delay bin. On average, over 92% of the total energy is confined within 100 ns. This means that a PRI greater than 100 ns would experience
very little inter symbol interference (ISI). In addition, over 99% of the total energy arrives within 160 ns. We set the value of PRI to be above 200 ns for our system to avoid ISI.
As described previously, we intend to estimate the delay τ that maximizes Ryx.
We therefore perform the peak detection on Ryx to obtain an estimate for the TOA
and hence the distance of the target. In Fig. 5.7(b), we compare the ranging perfor- mance based on the mean TOA from the received signal and the TOA determined via the peak detection of Ryx. Apparently, this result indicates the ranging error
with respect to the TOA variation caused by the multipath dispersion. As seen from the figure, at low SCNRs the error using TOA obtained from the peak detection of Ryx is smaller than the one using the mean TOA. We also compare this result with
the Cramer-Rao Lower Bound (CRLB) for the TOA estimation method in [149]. It can be found that the proposed scheme achieves errors close to CRLB at high SCNRs.
Fig. 5.7(c) represents the effect of PRI on the ranging performance. As seen from this figure, the ranging error sharply increases as we decrease the PRI below 200 ns. This complies with the previous analysis on the delay interval within which the incoming signal power is confined. Finally, the proposed technique outperforms the method based on the mean TOA throughout the entire range of PRI.
(a)
(b)
Figure 5.1: (a) Coexistence of communication and radar functionalities in a CRR network, and (b) CRR node architecture.
Figure 5.2: (a)column vector of the Walsh-Hadamard code matrix, (b) 16-bit CRR transmission waveform, (b) orthogonality of CRR waveforms, and (c) spectrum of the received CRR signal at a reference distance of 20 m at 4 GHz center frequency.
Figure 5.3: (a) Static target and non-target (clutter) scatterers resolved after 10 iterations of MI minimization at a CRR node, and (b) target and clutter returns after 10 iterations of MI minimization.
Figure 5.4: (a) Minimization of MI algorithm at different SNRs, (b) probability of target detection against SCNR for various iterations of the MI minimization algorithm, and (c) probability of detection for waveform selection based on MI min- imization and static waveform assignment.
Figure 5.5: (a) BER of different joint communication-radar waveform designs, and (b) throughput performance against distance from a particular CRR node.
Figure 5.6: (a) Target range profile for a target velocity = 3.5 m/s for 4 s time duration after 10 iterations of MI minimization, and (b) target and clutter returns after 10 iterations of MI minimization.
Figure 5.7: (a) UWB channel model, (b) average ranging error based on TOA estimation in the multipath UWB channel, and (c) average ranging error against PRI in the multipath UWB channel.
5.6
Chapter Summary
In this chapter, a joint communication-radar waveform design solution for the CRR network is developed. As indicated by the simulation results, the CRR waveform optimization approach promises better target impulse response extraction and range resolution. From a communications perspective, the proposed CRR waveform de- sign also promises high data rate performance over short ranges. The radar and communication signals share the same spectral and temporal domains using the current design strategy. This approach was based upon constant learning of the target environment and adapting the transmission waveform characteristics to suit the dynamic target scene. Such a cognitive approach ensures maximum information extraction from the radar scene and better target discrimination capability. The
proposed unified system would constitute a unique cost-efficient platform for future intelligent surveillance applications, for which both environment sensing along with the allocation of ad hoc communication links are essential. Such systems can be used in mission-critical and military applications for addressing the remote surveillance and communication issues simultaneously. It is envisaged that the future personal communication and tracking devices will have comprehensive radar-like function, such as spectrum sensing and localization, in addition to multi-mode and multi- band communication capability.
Chapter 6
Location Aware Spectrum and
Power Allocation Algorithm for
Cognitive Wireless Systems
In this chapter, a novel approach to spectrum and power allocation is proposed for joint cognitive communication-radar networks, which aim at integrating cognitive radio and cognitive radar paradigms to achieve intelligent utilization of spectrum resources in wireless networks. The CRR nodes discussed in chapter 5 are mobile radar units capable of extracting target parameters in the radar environment and are able to simultaneously exchange communication data over the CRR network. The communication functionality was an added feature to the CRR node design. On the other hand, the CRR units, proposed in this chapter are wireless devices which have the main purpose of exchanging data over the network. Although they will benefit from the physical location information provided by the cognitive radar aspect of the CRR design, exchange of data over the network is the primary function served by the CRR nodes in the network. For example, the CRR network presented in chapter 5 could be applied to a battlefield scenario in which the soldiers could carry hand- held wireless devices or CRR units, capable to track down a mobile target and at the same time exchange vital radar scene information through the communication link. Whereas in this chapter, the CRR nodes represent the wireless devices like routers and hubs, which have a sole purpose of exchanging data over the network and are empowered with location information on other devices and users through the cognitive radar component in their design. Thus CRR nodes described in this
chapter are primarily radio units and the CRR nodes mentioned in chapter 5 are mainly radar units.
This approach exploits the location information offered by cognitive radar com- bined with spectrum sensing capability of cognitive radio to aid spectrum and power allocation by minimizing harmful interference among neighboring devices. Such sys- tems require both coexistence and sharing of perception of radio environment and radar scene. To offer better spectrum resource utilization, entropy of the received signal is employed in order to detect spectrum holes over the network topology. This entropy-based technique also demonstrates superior performance as compared to the conventional method based on energy detection. Simulation results indicate both throughput improvement and interference reduction among neighboring devices.
After designing the approach to spectrum and power allocation for the CRR network, the second aspect of this chapter is to investigate the inclusion of a cognitive mechanism in predicting the spectral holes over the CRR network, by adopting a HMM learning approach. Such a cognitive mechanism would enhance the overall throughput of the entire network, since the wireless devices operating with the CRR nodes would now be able to utilize the white spaces in the spectrum. To realize opportunistic spectrum access, spectrum sensing is applied to detect the presence of spectrum holes. If SUs randomly or sequentially sense the channels until a spectrum hole is detected, significant amount of the scarce spectrum resource will be wasted, since SUs transmit only after a decision has been made. On the other hand, with the use of an intelligent predictive method, SUs can learn from the past activities of PUs on each channel to predict the next channel state. By prioritizing the order in which channels are sensed according to the channel availability likelihoods, the probability that an SU gets a channel upon its first attempt significantly increases, thus reducing the possible waste. Simulation results indicate improvement in throughput and reduction in interference between neighboring wireless devices.
This inclusion of the cognitive mechanism for opportunistic spectrum access over the CRR network facilitates the fusion of the cognitive radar and cognitive radio paradigms. Such a fusion could achieve efficient power and spectrum resource allocation in a wireless network.