Survey of RF Communications and Sensing Convergence Research <s> V. STATE OF THE ART <s> This detailed guide clearly and concisely presents radar digital signal processing for both practicing engineers and engineering students. This revised edition of Fundamentals of Radar Signal Processing provides in-depth coverage of radar digital signal processing (DSP) fundamentals and applications. It has been updated to include coverage of measurement accuracy and target tracking. Additionally, to make it more useful as a teaching tool, it now includes end-of-chapter problems and a solutions manual. New to this Edition: New chapter on Measurement Accuracy and Target Tracking Two new appendices--Important Digital Signal Processing Facts; Important Probability Density Function and Their Relationships Addition of 20 to 30 problems to ends of chapters Solutions manual <s> BIB001 </s> Survey of RF Communications and Sensing Convergence Research <s> V. STATE OF THE ART <s> 1. History 2. Notational and mathematical preliminaries 3. Probability and statistics 4. Wireless communications fundamentals 5. Simple channels 6. Antenna arrays 7. Angle-of-arrival estimation 8. MIMO channel 9. Spatially adaptive receivers 10. Dispersive and doubly dispersive channels 11. Space-time coding 12. 2x2 network 13. Cellular networks 14. Ad-hoc networks 15. Medium access control protocols 16. Cognitive radios 17. Multiple antenna acquisition and synchronisation 18. Practical issues. <s> BIB002
In this section, we investigate the state of the art of joint radar-communications, or more broadly joint sensingcommunications. We look at systems that employ cognitive techniques, joint waveform/coding design, user subscription, passive communications, and passive radar, among others. FIGURE 10. Co-design block diagram. Radar and communications systems are designed jointly from the ground up. Nodes can be capable of either function, and the systems greatly benefit from the existence of one another. For example, radar functionality provides a more sophisticated channel estimation mechanism for communications equalization tasking. Note the systems do not need to be co-located or developed as a single piece of hardware, but must be designed to optimize performance with respect to both functions. These solutions can typically be delineated by waveform. This can include what waveforms for each user help reduce mutual interference, and joint waveform designs that accomplish both sensing and communications. Transmitted waveforms are often central to modern joint radar-communications system design due to the sensitivity of both users to the choice of waveform. An example of this fundamental tradeoff is given in Figure 11 . This is the autocorrelation function of two types of waveforms: a linear frequency-modulated (FM) chirp typical of radar systems (shown in blue), and an orthogonal frequency-division multiplexing (OFDM) waveform used often in modern communications (shown in orange). In the full autocorrelation, it is immediately clear that the OFDM presents significant ambiguity due to the cyclic prefix. For ranging of targets, this could be catastrophic, as the sidelobes are only 10-12 dB down from the main lobe, and very far away. Further, the autocorrelation is data dependent, and can vary significantly. This also affects the peak-toaverage power ratio (PAPR) in a data-dependent way, which can cause significant issues for radar power circuitry  . The communications user is agnostic to this behavior, as pilot signals are used for alignment, and cyclic prefix is discarded to mitigate channel fading BIB002 . The linear FM chirp, on the other hand, would make a poor communications signal, as it contains no modulation where data could be encoded. However, it provides superior range estimation for global error scenarios. If we look at the main lobe function up close in the bottom part of the figure, we see the OFDM main lobe is nearly 2x narrower. This means for high signal-tonoise ratio (SNR) tracking scenarios, the variance of the radar measurement is much better for the OFDM waveform, at least for this particular bit pattern. Another way to look at the problem is shown in Figure 12 , which shows the full complex ambiguity function magnitude for both waveforms as a contour plot. Here, we see that linear FM chirps have range-Doppler coupling that further FIGURE 11. Normalized autocorrelation function for the linear FM chirp (blue), and an OFDM waveform (dashed orange). The cyclic prefix in the OFDM waveform presents as significant ambiguity at non-zero time delays, presenting a challenge for radar processing in low SNR scenarios. However, the main lobe is narrower by nearly a factor of 2, indicating a significant reduction in range estimation variance in high SNR regimes, due to the noise-like properties of the data-driven OFDM waveform. The linear FM chirp has a wider main lobe, but well-behaved and fast decaying sidelobes. However, they convey no information for arbitrary data transfer. complicate processing, requiring compensation or the full complex ambiguity function calculation BIB001 . However, this can be seen as being Doppler resilient, and so may ultimately benefit over OFDM systems. In the case of the OFDM waveform, where the ambiguity function is shown in orange, the noise-like properties of the waveform result in a near deltafunction correlation shape. This can be beneficial for radar processing when ignoring global error, but may complicate matched filtering by adding significant straddle loss and driving significant processing power BIB001 . This simple trade illustrates the importance of waveform design, which is why much of the recent work in joint radar-communications has been focused on optimizing a joint waveform. We now discuss some of these systems in more detail and other state of the art methods achieving RF convergence.